Recommender systems (RS) help users to find their appropriate items among large volumes of information. Among the different types of RS, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is. In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson Correlation Coefficient. The proposed solution aims at taking advantage of content and contextual information in the recommendation process. We evaluate and show effectiveness of our approach on three different contextual datasets and analyze the performances of the variants of our approach based on the characteristics of these datasets, especially the sparsity level of the input data and amount of available information.
CARSs aim to take into account the users' contextual information, in the most efficient way, in order to propose more relevant and personalized recommendations @cite_19 . So instead of the 2D rating function of traditional RSs ( @math ), in CARSs we have the multidimensional function, @math @cite_26 . The context of a user is composed of a number of context factors like . To each one of these context factors some values can be associated, called context conditions. For example possible context conditions for could be and , and for could be . The integration of contextual information in CARSs can be done by relying on either , or @cite_19 :
@cite_19 @cite_26
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With the increasing use of connected devices and IoT, users’ contextual information is more and more available and used in different information systems. One of the domains where the use of contextual information is promising is that of recommendation. As a matter of fact, context-aware recommender systems (CARSs) have demonstrated that taking contextual information about users into account can improve the effectiveness of recommendation, by generating more relevant recommendations to the users in their specific contextual situation. In this paper we propose a new context representation and approach to integrate this kind of information into a recommender system. We make a strong representation of the context, based on the influence of context on ratings, calculated using the Pearson Correlation Coefficient. We do a pre-filtering recommendation based on this representation. Our evaluations demonstrate that our approach can outperforms the state of the art. In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information. Recommender systems (RS) play an important role in many areas including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has attracted many researchers to focus on designing systems that produce personalized recommendations in accordance with available contextual information of users. Comparing with traditional RS which mainly utilize users’ rating history, the context# aware recommender systems (CARS) can result in better performance in various applications. In this paper, we present a context#aware recommender system that extracts contextual information from a textual description of user current situation and use it in combination with user ratings history to compute a utility function over the set of items. The item utility shows how much it is preferable regarding user current context. In our system, the context inference is modeled as a supervised topic#modeling problem in which the set of categories for a contextual attribute constitutes the topic set. As an example application, we used our method to mine hidden contextual data from customers' reviews for hotels and use it to produce context#aware recommendations. Our evaluations suggest that our system can help produce better recommendations in comparison to a standard kNN recommender system. Context-aware recommender systems (CARS) aim at improving users’ satisfaction by tailoring recommendations to each particular context. In this work we propose a contextual pre-ltering technique based on implicit user feedback. We introduce a new context-aware recommendation approach called user micro-proling . We split each single user prole into several possibly overlapping sub-proles, each representing users in particular contexts. The predictions are done using these micro-proles instead of a single user model. The users’ taste can depend on the exact partition of the contextual variable. The identication of a meaningful partition of the users’ prole and its evaluation is a non-trivial task, especially when using implicit feedback and a continuous contextual domain. We propose an o-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem.