Shoujin Wang
Charu Aggarwal
ABSTRACT
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.
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Index Terms
- Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
Recommendations
News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender SystemsNews recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
Inter-Session Modeling for Session-Based Recommendation
DLRS 2017: Proceedings of the 2nd Workshop on Deep Learning for Recommender SystemsIn recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of ...
Combining User-Based and Session-Based Recommendations with Recurrent Neural Networks
Neural Information ProcessingAbstractRecommender systems generate recommendations based on user profiles, which consist of past interactions of users with items. When user profiles are not available, session-based recommendation can be used instead to make predictions based on ...
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