PSL-Based Interpretable Generation Model for Recommendation

Nowadays, recommendation systems have been widely used in various aspects such as news, movies, music, videos, academia, and many more. The advent of personalized recommendation systems has significantly enhanced the efficiency of users’ access to information and also improved their overall experience. As an essential component of the recommendation system research field, interpretable recommendations emphasize the need to provide users with recommended results along with the rationale behind them. Unlike traditional recommendation systems, interpretable systems can not only enhance system transparency but also increase user trust and acceptance, the likelihood of users choosing the recommended product, and overall satisfaction. However, most existing interpretable recommendation systems depend on user similarity, item similarity, scoring data, or review a single aspect of the data to produce an interpretation, which makes it challenging to create trustworthy interpretable due to the limited use of these factors and intelligent reasoning. To solve this problem, we propose the Probabilistic Soft Logic (PSL)-based Interpretable Generation Model for Recommendation (called PIGM). Unlike traditional interpretable recommendation models, our PIGM takes into account user similarity, item similarity, and scoring data, employing PSL to model these factors and utilizing intelligent reasoning to generate interpretations. Firstly, based on matrix decomposition to predict the user’s score of the item. Secondly, the data is partitioned into observation dataset, target dataset and fact dataset. Thirdly, predicates are defined according to the data and the required results, and rules are defined according to the defined predicates, fact logic and relevant knowledge generated by recommendation system interpretation. Then, the weights of the rules are learned based on the maximum likelihood estimation. Finally, based on PSL reasoning and according to the defined rules, generate a recommendation list and corresponding interpretation for the user. The experimental results demonstrate the effectiveness of our proposed PIGM model on the real dataset.