Haut PDF A hybrid model for context-aware proactive recommendation

A hybrid model for context-aware proactive recommendation

A hybrid model for context-aware proactive recommendation

Abstract Just-In-Time recommender systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user’s inter- ests at the right time. Our work falls within this framework and focuses on developing a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users’ personal interests at the right time without waiting for the users to initiate any interaction. Indeed, the devel- opment of mobile devices equipped with persistent data connections, geolocation, cameras and wireless capabilities allows current context-aware recommender systems (CARS) to be highly contextualized and proactive. Nevertheless, this requires to know how to efficiently combine the context dimensions. Several dimensions of context, such as location, time, users activities, needs, resources, light, noise, movement, etc., have to be managed and represented which require a big amount of information and are time consuming. Besides, the incorporation of too many context dimensions generate complex context models. On the other hand, context models integrating few dimensions are unable to figure out the whole user context.
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Context-Aware Adaptive Framework for e-Health Monitoring

Context-Aware Adaptive Framework for e-Health Monitoring

1) Framework Description: the framework of the proposed approach is presented in Fig. 1. The system uses the el- derly activity context as a base for sensing, analyzing, and making service recommendation. The person’s environment is equipped with a list of possible sensors which are placed in appropriate spaces. Data is obtained either continuously or periodically, then transmitted to the coordinator to be analyzed. A data management system manages data coming from these several heterogeneous sources. It supports all the usual database primitives (e.g. add, delete, search, query, etc.). The analyzing agent loads the person’s profile including dependency-context (D.C.) and history-context (H.C.) from the data management system during a specified period. The analyzing agent connects to the model-base management, then the first inference is performed to start/configure sensors and set up the monitoring mode. The model-base management is used to select the suitable models (e.g. SMAF [16] and AGGIR [7]) for the person’s activities and his behavior, which in turn is provided to the analysis agent and data management to retrieve input data and set up outputs for monitoring. Finally, after sensing and analyzing, the health services are recommended, based on the real needs of the monitored person.
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Exploring Current Viewing Context for TV Contents Recommendation

Exploring Current Viewing Context for TV Contents Recommendation

Tunis, Tunisia Email: Rim.Faiz@ihec.rnu.tn Abstract—Due to the diversity of alternative programs to watch and the change of viewers’ contexts, real-time predic- tion of viewers’ preferences in certain circumstances becomes increasingly hard. However, most existing TV recommender systems used only current time and location in a heuristic way and ignore other contextual information on which viewers’ preferences may depend. This paper proposes a probabilistic approach that incorporates contextual information in order to predict the relevance of TV contents. We consider several viewer’s current context elements and integrate them into a probabilistic model. We conduct a comprehensive effectiveness evaluation on a real dataset crawled from Pinhole platform. Experimental results demonstrate that our model outperforms the other context-aware models.
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Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning

Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning

In Table 1 , IR Scoring and IR And indicate the improving rate achieved using the “Scoring” and the “And” operators respectively. According to Table 1 , our proposed approach is able to outperform the baselines by achieving higher prediction accuracy. More precisely, our model based “Scoring” operator allows achieving a considerable improvement of +72.1%, +72.9% and +62.4% over Agg, CIC and CCA models respectively. The same trend of improvement holds for the model based on the “And” operator. These results could be explained by the fact that the multi-criteria Agg, CIC and CCA models use either a traditional way for predicting multi-criteria ratings, a linear aggregation, or both which may decrease prediction accuracy. The multi-criteria algorithm based on clustering (CluAllCrit) which uses a linear aggregation degrades the prediction results com- pared with other multi-criteria algorithms. Therefore, our model allows a huge improvement over CluAllCrit (+482.4% by the “Scoring” operator and +434.7% by the “And” operator), this may be because the problem with the automatic cri- teria coefficients obtained by the linear aggregation function. Even when employ- ing a clustering technique to enhance prediction results, using such coefficients in the aggregation process may generate many rating prediction results with nega- tive values or outside of the [1..5] scale. Comparing with the CCC model, which considers criteria dependency to predict the criteria ratings and uses conditional aggregations, there is a little difference in the accuracy results between this lat- ter model and ours. These results reveal that there might exist complementary criteria affecting the user’s choice for choosing an item. Meanwhile, using a con- ditional aggregation may not always be a good choice, since CIC model which uses a conditional aggregation performs worse than CCA model which uses a linear function.
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DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

hybrid network structure (Wide&Deep) that combines a lin- ear (wide) model and a deep model. In this model, two different inputs are required for the “wide part” and “deep part,” respectively. However, the input of wide part still relies on expertise feature engineering. In order to reduce manual feature engineering, Guo et al. [19] proposed a new neural network model DeepFM, which integrates the architectures of FM and DNNs. It can model low-order feature interac- tions like FM and models high-order feature interactions like DNN. However, Wide&Deep and DeepFM just model high- order feature interactions implicitly, since the function learned by DNNs can be arbitrary. Furthermore, Wang et al. [20] proposed the deep and cross network (DCN) model, which can learn high-order feature interactions implicitly and explic- itly. Particularly, DCN contains a CrossNet that can capture feature interactions of bounded degrees. However, DCN mod- els feature interactions at the bit-wise level, which is different from the traditional FM framework which models feature interactions at the vector-wise level. The details about bit- wise and vector-wise feature interaction will be introduced in Section IV. Recently, Lian et al. [21] proposed a new model, named xDeepFM, which can learn explicit and implicit high-order feature interactions effectively. In particular, they designed a compressed interaction network (CIN), which gen- erates feature interactions in an explicit fashion and at the vector-wise level.
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Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning

Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning

In Table 1, IR Scoring and IR And indicate the improving rate achieved using the “Scoring” and the “And” operators respectively. According to Table 1, our proposed approach is able to outperform the baselines by achieving higher prediction accuracy. More precisely, our model based “Scoring” operator allows achieving a considerable improvement of +72.1%, +72.9% and +62.4% over Agg, CIC and CCA models respectively. The same trend of improvement holds for the model based on the “And” operator. These results could be explained by the fact that the multi-criteria Agg, CIC and CCA models use either a traditional way for predicting multi-criteria ratings, a linear aggregation, or both which may decrease prediction accuracy. The multi-criteria algorithm based on clustering (CluAllCrit) which uses a linear aggregation degrades the prediction results com- pared with other multi-criteria algorithms. Therefore, our model allows a huge improvement over CluAllCrit (+482.4% by the “Scoring” operator and +434.7% by the “And” operator), this may be because the problem with the automatic cri- teria coefficients obtained by the linear aggregation function. Even when employ- ing a clustering technique to enhance prediction results, using such coefficients in the aggregation process may generate many rating prediction results with nega- tive values or outside of the [1..5] scale. Comparing with the CCC model, which considers criteria dependency to predict the criteria ratings and uses conditional aggregations, there is a little difference in the accuracy results between this lat- ter model and ours. These results reveal that there might exist complementary criteria affecting the user’s choice for choosing an item. Meanwhile, using a con- ditional aggregation may not always be a good choice, since CIC model which uses a conditional aggregation performs worse than CCA model which uses a linear function.
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Context Aware Recommender Systems for Tourism: A Concise Review

Context Aware Recommender Systems for Tourism: A Concise Review

are available such as feedback items details and contextual information, the hybrid approach can be used to improve the performance of the recommender system [1]. The hybrid approach combines different recommendation algorithms to improve the performance through using multiple input data like context, items characteristics and users’ feedback. Actually, at the emergence of recommender systems, they all relied on users’ profiles and items descriptions to produce recommendations. However, later contributions have proposed the use of context information in order to make refined and relevant recommendations that are suitable to the target users’ context (weather, location...). In this paper, we will present context aware recommendation systems that have been implemented in the tourism domain recently.
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Context-aware recommender systems for real-world applications

Context-aware recommender systems for real-world applications

Another framework for hotel recommendation was proposed in [ Zhang et al. , 2015 ] and leverages textual reviews and ratings. The authors propose a hybrid approach where users and hotels are first modeled in latent topic spaces based on the reviews given by users for hotels. Similarities between users and hotels are then derived from these models. Rating prediction is performed by using Matrix Factorization (MF) and by adding a constraint that enforces the learned models for each pair of users and hotels to be close if their com- puted similarity is high. The predicted ratings are then modified according to the user’s travel intent that is explicitly provided through the proposed framework. Finally, diversity techniques are used to optimize the ranking of the recommended list by removing redun- dancies while maintaining relevance. To validate the proposed approach, experiments were conducted on a dataset from the travel search engine Ctrip.com, and errors in rating pre- diction were reported. On another note, explicit feedback in the form of ratings assigned to various hotel aspects, e.g., location, cleanliness, services, and rooms, were also exploited for hotel recommendation. In [ Nilashi et al. , 2015 ], a 3-dimensional tensor is created by as- sociating the first dimension with users, the second one with items, and the third one with hotel aspects. The tensor is used to cluster users and a dimensionality reduction technique is applied within each cluster. Neural networks integrating fuzzy logic principles are then trained to predict overall ratings in each cluster. Experiments were conducted on datasets from Tripadvisor.com and proved the effectiveness of the proposed method and its several components for improving the accuracy of multi-criteria prediction.
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A Property Graph Data Model for a Context-Aware Design Assistant

A Property Graph Data Model for a Context-Aware Design Assistant

{philippe.veron 2 , frederic.segonds 3 }@ensam.eu 4 thomas.zynda@capgemini.com Abstract. [Context] The design of a product requires to satisfy a large number of design rules so as to avoid design errors. [Problem] Although there are numerous technological alternatives for managing knowledge, design departments continue to store design rules in nearly unusable documents. Indeed, existing propositions based on basic information retrieval techniques applied to unstructured engineering documents do not provide good results. Conversely, the development and management of structured ontologies are too laborious. [Proposition] We propose a property graph data model that paves the way to a context-aware design assistant. The property graph data model is a graph-oriented data structure that enables us to formally define a design context as a consolidated set of five sub-contexts: social, semantic, engineering, operational IT, and traceability. [Future work] Connected to or embedded in a Computer Aided Design (CAD) environment, our context-aware design assistant will extend traditional CAD capabilities as it could, for instance, ease: 1) the retrieval of rules according to a particular design context, 2) the recommendation of design rules while a design activity is being performed, 3) the verification of design solutions, 4) the automation of design routines, etc.
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Just-In-Time Recommendation Approach Within A Mobile Context

Just-In-Time Recommendation Approach Within A Mobile Context

Tunis, Tunisia Email: rim.faiz@ihec.rnu.tn Abstract—Just-In-Time Recommender Systems involve all sys- tems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user’s interests at the right time. In this paper, we propose a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users’ personal interests at the right time without waiting for users to initiate any interaction.
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Exploring Current Viewing Context for TV Contents Recommendation

Exploring Current Viewing Context for TV Contents Recommendation

Keywords: Context-based, TV-Recommender systems, Probabilistic model I. I NTRODUCTION Recently, context-aware recommender systems play a crit- ical role in different domains such as events, locations and music recommendation. Generally, their effectiveness is due to the integration of additional information that define the specific situation under which recommendations are made. For example, a user might prefer to watch world news (e.g. CNN or BBC) in the morning with colleagues and movies recommended by friends on weekends.

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en fr Context-aware radiation protection for the hybrid operating room Méthodes de radioprotection réactives au contexte pour la salle d’opération hybride

combining radiation simulation, person tracking and visualization of radiation in virtual environments have been developed. We hereby present some examples of such systems which have settled the foundations for the work performed in this thesis. [Ladikos 2010] presents a system to sensitize physicians and allow them to review their radiation exposure after a procedure. A radiation simulation framework using Geant 4 [Agostinelli 2003] is used to simulate scattered radiation and the results are displayed as a color-coded heat map overlaid over a 3D mesh representation of a person’s shape. 16 optical cameras mounted on the ceiling are used together with a background subtraction and shape-from-silhouette approach to reconstruct and track the 3D mesh of a person. The position of the C-arm is determined offline and the distribution of radiation in the environment is computed by placing detector spheres around the scene in the simulation and registering the energy of the particles which fall onto them. Such an irradiation volume is pre-computed and is later composed with the tracked physician’s mesh in order to accumulate the radiation received by each vertex and, by interpolation, the radiation received by the whole mesh. As shown in figure 2.5a, the exposure of a person moving around the room can be visualized this way. While this work has introduced the concept of visualizing the radiation risk overlaid on a person, the simulations neither take all parameters of the scattered radiation production nor the room configuration into account. For instance, the simulation model does not include the operating table which also affects the scattered radiation distribution. The real X-ray source parameters (peak kilovoltage, filtration, field-of-view) are not fully considered, and results are shown for a single C-arm configuration only. The validity of the proposed visualizations is also not verified experimentally. Moreover, the system is not designed for intraoperative use. Such a tracking approach based only on color images would not always be possible in a real interventional scenario since many procedures are carried out with the lights off for a better visibility of the X-ray images displayed on the screen. The dynamic nature of that environment along with the possible multiple occlusions would also be a challenge for the background subtraction approach and for the precise mesh tracking required by the proposed visualization.
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A Mobile Context-Aware Proactive Recommendation Approach

A Mobile Context-Aware Proactive Recommendation Approach

4.1 The TREC 2014 Contexual Suggestion Track This task offers an evaluation platform for search techniques that depend highly on the context and the user interests. The input to this task consist of a set of profiles, a set of sample suggestions (a set of venues evaluated by the profiles) and a set of contexts. Each profile corresponds to a single user, and indicates the preference of the user with respect to the set of suggestions. For example, one suggestion could be a recommendation to have a beer at the Dogfish Head Alehouse. The profile describes the negative or the positive preference of the user regarding the set of suggested venues.
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A Mobile Context-Aware Proactive Recommendation Approach

A Mobile Context-Aware Proactive Recommendation Approach

geo relevance = N b Geo Relevant V enues |V g | (5) To measure the profile relevance, there were two alternatives to note. A first alternative is to consider for each context, the intersection of our venues’ collec- tion with the venues provided by each run 5 , however, this intersection almost gave the empty set. We opted for an intermediate solution of considering the intersection of our venues’ collection with the union of the venues that each run has proposed across all profiles in order to get the suggested venues ratings. The cardinality of this intersection is |V p | = 889 venues.
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Context factors in context-aware recommender systems

Context factors in context-aware recommender systems

I. I NTRODUCTION The available data and information on the web is be- coming increasingly important while the users can easily be overwhelmed by these data and information. It is why we need strong filtering techniques to retrieve the appropi- ate information. One of these techniques is that based on recommendation. Recommender systems propose items that can potentially be interesting for the user. Several traditional recommender systems like Amazon and Netflix have proven their reliability through the years. Their recommendations are essentially based on users’ rankings on items. In these recent years, a new recommendation approach has emerged called context-aware recommendation. Such approaches try to improve the relevance of recommendations by adding some additional information like the actual context of the user. [1] founds a correlation between the user behaviour and his/her context, which explains the importance of integrating the user context in the recommendation process.
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Context Aware Adaptable Applications - A global approach

Context Aware Adaptable Applications - A global approach

Chara cterizat ion Figure 4 : Context class diagram 2.2 Context and applications Since several years, the natural evolution of applications to distribution shows the need of more than only processing information. Traditionally, applications are based on input/output, i.e. input data given to an application produces output data. This too restrictive approach is now old fashioned [48] . Data are not clearly identified, processes does not only depend on provided data but depend also on data such the hour, the localization, preferences of the user, the history of interactions, etc. in a word the context of the application. We can find a representative informal definition in [49] "The execution context of an application groups all entities and external situations that influence on the quality of service/performances (qualitative & quantitative) as the user perceives them".
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Context-aware decentralized approach for web services

Context-aware decentralized approach for web services

3.3 WebID One of the most important aspects of our proposal deals with authentication in the Semantic Web. A very interesting so- lution we decided to adopt comes in the form of WebID [12], an authentication system based on FOAF and TLS. We have chosen WebID because it helps alleviate the difficulty of re- membering different logins and passwords combinations that users face when authenticating on multiple websites. WebID’s simplifications create a cascade of benefits. Being a Web Architecture compliant protocol, trust can be moved from the Identity Provider to the Web of relations. This ap- proach would in fact address the issues present in federated identity management systems, described in Section 2.2. Please consider the following example of WebID-based au- thentication process, described in Figure 1. The three key elements in this example are User 1 (i.e. the user’s browser),
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CACDA: A knowledge graph for a context-aware cognitive design assistant

CACDA: A knowledge graph for a context-aware cognitive design assistant

Fig. 1. Context-aware cognitive design assistant. of different things and attempts to provide a definition : “Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered rel- evant to the interaction between a user and an application, including the user and applications themselves”. A context includes two kinds of information: domain specific and activity specific ( Cassens and Kofod-Petersen, 2006 ). Domain specific information represents the working environment of the user. It does not evolve according to the user’s actions. Activity specific information represents the real time activity of the user. It is therefore evolving according to the user’s actions. Da Cunha Mattos et al. (2014) propose a formal represen- tation for context-aware businesses. In the field of manufacturing, context-aware approaches enable workers to receive manufactur- ing information according to their job and experience ( Dhuieb et al., 2016 ). Although manufacturing differs from design, this example provides us with some details on the definition of the context that includes three viewpoints: operational (activities and tasks of the worker), organisational (team and role of the worker), and user- centric (expertise and skills). Related to our research goal, Rowson et al. ( Rowson et al., 2018 ) investigate the idea of building reusable expert knowledge using screen monitoring and contextual simi- larity. Nevertheless, the authors assume too many aspects of the framework, such as the form and the content of the knowledge base, the way information is collected, the query language and pat- terns, the similarity measure, etc. Moreover, we can notice that the context is limited to the interaction of the designer with the CAD software.
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Spatial models for context-aware indoor navigation systems: A survey

Spatial models for context-aware indoor navigation systems: A survey

Whereas geometric models can efficiently integrate metric properties to provide highly accurate location and distance information that are necessary elements in most of context-aware app[r]

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Context-aware mechanisms for device discovery optimization

Context-aware mechanisms for device discovery optimization

Chapter 6. Conclusions and Perspectives 112 process by defining an optimized transmission probability for each discovery configura- tion. The transmission probability for direct discovery is defined by the discovery re- source pool, and for out-of-coverage context, it is predefined in the devices. So, the UEs are able to transmit discovery messages using that static threshold. The study demon- strated that the suggested values did not always provide the best discovery performance and that for a given number of users performing discovery and allocated resources there was an optimized value to use. The study also considered a fixed number of UEs, sim- plified propagation models, and the discard of all colliding discovery messages, which, while not usually the case for real communications, proved to be a solid foundation. We tried to make use of the generated equations and developed an adaptive algorithm that updated the number of UEs discovered through time and calculated the optimized transmission probability at each point of time. The UEs used the new computed value and transmitted discovery messages accordingly. For validating this mechanism, we first simulated the discovery process using the same assumptions as the analytical model, and we showed that the discovery process ended faster using our proposed algorithm, compared to the traditional discovery process defined in 3GPP standards. Then, we considered more realistic assumptions (i.e. propagation loss and attempts of recovery in case of collisions). Although the UEs took longer to finish the discovery process (given the increased number of collisions and errors), our proposed algorithm still behaved bet- ter than the standard-defined discovery process. We also validated that adding UEs to already discovered groups, and observed that the algorithm succeeded in better account- ing for the change in the group’s topology, which translated in the UEs detecting the newcomer faster than the discovery according to the 3GPP specifications. Therefore, our proposed algorithm was dynamic and adaptive, and it managed to save transmis- sion resources by striking a balance between the benefits of saturating the channel with messages and the cost of message collisions.
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