# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
## 2 Related Work
### Traditional Tag-Aware Recommender Systems  
Many traditional methods can be applied to TRS. Among them, collaborative filtering (CF) is the most widely used technique. Extending the CF to TRS, [15] [PERSON] et al. introduced a contextual CF model which takes tagging information into consideration when calculating the user similarity between users and predicting ratings for the user. Another work [16] was proposed by [PERSON] et al., who reduced the ternary relation of the _user-item-tag_ to a lower-dimensional space, and then used CF to generate recommendation results. Based on this model, [PERSON] [17] developed a similar algorithm that finds user neighbors via the user-tag matrix and then recommends items liked by these neighbors to the target user. [PERSON] et al. [18] proposed a generic method that allows tags to be incorporated to standard CF methods, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to reassociate these correlations.  
To solve the problem of redundancy and ambiguity caused by tags, other kinds of solutions have been applied to TRS as well, such as clustering, tensor-based methods, graph-based methods, etc. In [1], [PERSON] et al. presented a personalized recommendation that relies on hierarchical tag clusters. Through clustering technique, redundant tags are aggregated, and a cluster is more easily detected than a single tag. [PERSON] et al. proposed an algorithm [19] to exploit the semantic contribution of tags. They extended the probabilistic latent semantic analysis (PLSA) approach and presented a unified recommendation model that evolving from co-occurrence theory. [PERSON] et al. proposed a method based on semantic web [20], they constructed a movie knowledge base and then predict ratings by the defined rating tag-clouds. [PERSON] et al. [21] developed a general framework by applying a semantic analysis and dimension reduction to the tensor _user-item-tag_ via higher order singular value decomposition (HOSVD). [PERSON] [22] proposed a tensor factorization algorithm RTF (ranking with tensor factorization) that improves optimization approaches. [PERSON] et al. were also inspired by the Google PageRank [23] algorithm and in turn proposed the FolkRank [24] algorithm, according to which users, items and tags reinforce one another's weights through the spreading of weights, from which items with higher weights are recommended. In [25], [PERSON] et al. integrated tagging information into _user-item_ bipartite graphs and proposed a model based on _user-item-tag_ tripartite graphs. PITF [26] is optimized from the Bayesian personalized ranking [27] criterion. It models the pair-wise interactions among users, items and tags in lower dimensional matrices to decrease noise. Similarly, [PERSON] et al. proposed a probabilistic model BTR (Bayesian-based Tag Recommendation algorithm) [28] to turn the tag recommendation task into a probability prediction problem so that a classical Bayesian method could be easily applied.  
Compared with TRSDL, firstly, these models did not take advantage of word embeddings to overcome the problem caused by tags; secondly, they still use shallow structures, while the ability of interpreting complex nonlinear relationships within data is weaker than those based on deep structures. Furthermore, they did not take into account the temporal sequence of the user's tagging behaviors, which may improve the recommendation performance.