Cd-HRNN: Content-Driven HRNN to Improve Session-Based Recommendation System

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.