Recommendation system could help the companies to persuade users to visit or consume at a particular place, which was based on many traditional methods such as the set of collaborative filtering algorithms. Most research discusses the model design or feature engineering methods to minimize the root mean square error (RMSE) of rating prediction, but lacks exploring the ways to generate the reasons for recommendations. This paper proposed an integrated neural network based model which integrates rating scores prediction and explainable words generation. Based on the experimental results, this model presented lower RMSE compared with traditional methods, and generate the explanation of recommendation to convince customers to visit the recommended place.
0.05in Collaborative Filtering is commonly used in recommender system @cite_12 @cite_11 @cite_16 @cite_2 --- exploiting similarity among the preference of users to generate recommendations. Research is done using users’ historical data to predict ratings, and deliver the results of recommendation to users @cite_26 @cite_9 . The traditional methods in rating prediction are the user-based model, and matrix factorization(MF). According to @cite_18 , they introduced the user-based method which predicts the rating scores based on the other users who have similar preferences. Regarding matrix factorization, mentioned the applications of two commonly used methods: Singular Value Decomposition(SVD) and Non-negative Matrix Factorization(NMF) @cite_18 . Additionally, many researchers utilized neural-based collaborative filtering model for rating prediction @cite_1 @cite_23 . However, there are many limitations for the traditional algorithms such as scalability problems, and lacking bias terms. As for neural collaborative filtering, it performs better on rating prediction, but there is no description of generating explainable text in empirical works.
@cite_18 @cite_26 @cite_9 @cite_1 @cite_23 @cite_2 @cite_16 @cite_12 @cite_11
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Abstract Recommendation Systems (RSs) are becoming tools of choice to select the online information relevant to a given user. Collaborative Filtering (CF) is the most popular approach to build Recommendation System and has been successfully employed in many applications. Collaborative Filtering algorithms are much explored technique in the field of Data Mining and Information Retrieval. In CF, past user behavior are analyzed in order to establish connections between users and items to recommend an item to a user based on opinions of other users. Those customers, who had similar likings in the past, will have similar likings in the future. In the past decades due to the rapid growth of Internet usage, vast amount of data is generated and it has becomea challenge for CF algorithms. So, CF faces issues with sparsity of rating matrix and growing nature of data. These challenges are well taken care of by Matrix Factorization (MF). In this paper we are going to discuss different Matrix Factorization models such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Probabilistic Matrix Factorization (PMF). This paper attempts to present a comprehensive survey of MF model like SVD to address the challenges of CF algorithms, which can be served as a roadmap for research and practice in this area. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past user-item relationships. In this work, we propose novel algorithms for predicting user ratings of items by integrating complementary models that focus on patterns at different scales. At a local scale, we use a neighborhood-based technique that infers ratings from observed ratings by similar users or of similar items. Unlike previous local approaches, our method is based on a formal model that accounts for interactions within the neighborhood, leading to improved estimation quality. At a higher, regional, scale, we use SVD-like matrix factorization for recovering the major structural patterns in the user-item rating matrix. Unlike previous approaches that require imputations in order to fill in the unknown matrix entries, our new iterative algorithm avoids imputation. Because the models involve estimation of millions, or even billions, of parameters, shrinkage of estimated values to account for sampling variability proves crucial to prevent overfitting. Both the local and the regional approaches, and in particular their combination through a unifying model, compare favorably with other approaches and deliver substantially better results than the commercial Netflix Cinematch recommender system on a large publicly available data set. We use a low-dimensional linear model to describe the user rating matrix in a recommendation system. A non-negativity constraint is enforced in the linear model to ensure that each user’s rating profile can be represented as an additive linear combination of canonical coordinates. In order to learn such a constrained linear model from an incomplete rating matrix, we introduce two variations on Non-negative Matrix Factorization (NMF): one based on the Expectation-Maximization (EM) procedure and the other a Weighted Nonnegative Matrix Factorization (WNMF). Based on our experiments, the EM procedure converges well empirically and is less susceptible to the initial starting conditions than WNMF, but the latter is much more computationally efficient. Taking into account the advantages of both algorithms, a hybrid approach is presented and shown to be effective in real data sets. Overall, the NMF-based algorithms obtain the best prediction performance compared with other popular collaborative filtering algorithms in our experiments; the resulting linear models also contain useful patterns and features corresponding to user communities.