A Hybrid Recommender System for Sequential Recommendation: Combining Similarity Models With Markov Chains

With the growth of artificial intelligence technology, the importance of recommender systems that recommend personalized content has increased. The general form of the recommender system usually analyzes the users' log information to provide them with contents that they are interested in. However, to enable users to receive more suitable and personalized content, additional information must be considered besides the user's log information. We develop, in the present study, a hybrid recommender system that unifies similarity models-collaborative and content-based-with Markov chains for a sequential recommendation (called U2CMS). U2CMS takes into account both sequential patterns and information about contents to find accurate relationships between items. It uses a higher-order Markov chain to model sequential patterns over several time steps, as well as the textual information of the content to model the recommender system. To show the effectiveness of the U2CMS-with regard to handling sparsity issues, different N-ordered Markov Chain, and accurately identifying similarities between items, we carried out several experiments on various Amazon datasets. Our results show that the U2CMS not only has superior performance compared to existing state-of-the-art recommendation systems (including deep-learning based systems), but also it successfully handles sparsity issues better than other approaches. Moreover, U2CMS appears to perform stable when it comes to different N-ordered Markov Chain. Lastly, through visualization, we show the success of our proposed content-based filtering model in identifying similar items.