Recommender systems used in online platforms can drive users to consume content continuously in an attempt to maximize satisfaction. Such engagement is invariably broken due to more pressing work, alternate pursuits, distractions or fatigue. Recommender systems need to ensure the continuity of experience when the user joins back. Session-based recommender systems typically create different sessions based on a fixed time interval (θ), often resulting in creation of a separate session when the user gets off the platform temporarily. When the user joins back, session-based recommender systems are likely to recommend content different than what they would have in case the earlier session had continued. This may cause dissatisfaction given that there is a difference in the predicted world model of the user, i.e. the expectation from the last session, and the observed one, i.e. the recommendations. To handle this problem, we propose the creation of content-driven sessions instead of time-driven sessions. In our setting, a session continues while a single item category dominates in the user-item interactions. A new session is created when a different item category begins to dominate. The proposed content-driven method also solves the long-standing problem of deciding the optimal value of time threshold (θ) for defining the time-based session. We report that the proposed method outperforms existing SOTA methodologies set by time-based sessions by a large margin in terms of recommendation performance on multiple datasets.

The increasing popularity of digital entertainment systems has made personalization a key factor for success in the industry. Recommendation systems, particularly for videos and movies, are crucial in this regard. However, many existing systems are implicit feedback recommendation system that uses indirect signals to infer user preferences, such as user actions (e.g. clicks, views, purchases) or interactions with items (e.g. listening to a song, watching a movie). The challenge lies in the limited information and uncertainty present in user behavior, making it difficult to predict their interests and preferences. In Previous research, Recurrent Neural Networks (RNNs) have shown to be efficient in predicting the next item in a session, based on past item click sequences, but their effectiveness is limited when only relying on click sequences as input data. In this paper, we extend the Hierarchical RNN architecture (HRNN) for generating recommendations by combining session clicks and item content information, such as item ids and item description respectively. The Bidirectional Encoder Representations from Transformers (BERT) architecture is applied for generating feature vectors from text descriptions of the items. Our model has been extensively tested on the benchmark dataset MovieLens 1m has demonstrated superiority over state-of-the-art (SOTA) session-based recommendation systems (SBRS) models. Experimental results establish the efficacy of using content information along with item ids for recommendation.