Abstract
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.
Highlights
The importance of recommender systems that recommend personalized contents has considerably increased during the past decade due to the rapid advancement in artificial intelligence technology
More details on comparison between the existing recommender models and our proposed model is given in Section V (Table. 3). To address these gaps, we develop a hybrid approach inspired by the Fossil and content-based Filtering approaches [5]–[8], which combines the similarity-based approach with the high-order Markov chain and the similarity between items
To show the efficiency of U2CMS, we compared its performance with the following advanced existing approaches (Table 3 shows the properties of each model): 1. Bayesian personalized ranking matrix factorization (BPR-MF) [45] is a top-K recommendation model based on MF, which models the user preference with two latent matrices
Summary
The importance of recommender systems that recommend personalized contents has considerably increased during the past decade due to the rapid advancement in artificial intelligence technology. Despite the successful results of the FPMC, this model considers only the previous order information of the user, and the calculation is complicated due to the separation of the sequential pattern matrix and the user preference matrix He and McAuley [3] presented a factorized sequential prediction with item similarity model called Fossil which includes the advantages of the aforementioned approach. PROBLEM DEFINITION AND NOTATIONS Traditional CF-based recommendation models do not consider the item information and the sequential pattern to predict the list of recommendations that a user might be interested in. They focus on the user data, such as the user’s profile.
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