Douban Movie Research Articles

Personalized Movie Recommendation based on Convolutional Neural Network

In pace with the development of the economy, the spiritual entertainment brought by movies is increasingly valued by people, and the problem of how to recommend the most suitable movie for users among the numerous movies also arises. Based on this, experiments were conducted using convolutional neural networks in the field of deep learning for movie recommendation. The convolutional neural network was trained using user information, movie information, and user movie rating data from the Douban Movie Network. In data preprocessing, instead of converting category fields to one hot encoding, they are converted into numbers and used as indexes for embedding matrices. After extracting features from the embedding layer, input these two features again in the fully connected layer, output a value, and then regress this value to the score to obtain MSE loss, and use the recall rate and NDCG indicator evaluation model. By comparing the accuracy with the other four recommendation algorithms on the same dataset, it was found that the movie recommendation algorithm obtained through the convolutional neural network approach studied in this paper has shown better results on the same evaluation indicators.

Personalized movie recommendations based on probabilistic linguistic sentiment and integrated DEMATEL-TODIM methods

With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.

Cross-domain information fusion and personalized recommendation in artificial intelligence recommendation system based on mathematical matrix decomposition

Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation lists.In the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k = 10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates clear advantages in accuracy, prediction performance, recommendation diversity, and ranking quality, and improves the accuracy and diversity of the recommendation system. By effectively addressing collaborative filtering challenges through the integration of diverse techniques, it significantly surpasses traditional models in recommendation accuracy and variety.

Dual disentanglement of user–item interaction for recommendation with causal embedding

To achieve personalized recommendations, the recommender system selects the items that users may like by learning the collected user–item interaction data. However, the acquisition and use of data usually form a feedback loop, which leads to recommender systems suffering from popularity bias. To solve this problem, we propose a novel dual disentanglement of user–item interaction for recommendation with causal embedding (DDCE). Different from the existing work, our innovation is we take into account double-end popularity bias from the user-side and the item-side. Firstly, we perform a causal analysis of the reasons for user–item interaction and obtain the causal embedding representation of each part according to the analysis results. Secondly, on the item-side, we consider the influence of item attributes on popularity to improve the reliability of the item popularity. Then, on the user-side, we consider the effect of the time series when obtaining users’ interest. We model the contrastive learning task to disentangle users’ long–short-term interests, which avoids the bias of long–short-term interests overlapping, and use the attention mechanism to realize the dynamic integration of users’ long–short-term interests. Finally, we realize the disentanglement of user–item interaction reasons by decoupling user interest and item popularity. We experiment on two real-world datasets (Douban Movie and KuaiRec) to verify the significance of DDCE, the average improvement of DDCE in three evaluation metrics (NDCG, HR, and Recall) compared to the state-of-the-art model are 5.1106% and 4.1277% (MF as the backbone), 3.8256% and 3.2790% (LightGCN as the backbone), respectively.

The Influence of Douban Reviews and Ratings on Chinese Audiences' Movie-watching Choices

In the process of word-of-mouth, recurring social media ratings and strong reviews can influence viewers' perceptions of a film and result in changes in viewing choices. As the most authoritative movie review website in China, Douban's high score and popular movie reviews are a household name, and even the rating of a movie on Douban and whether the review is good or not become the reason for people to choose whether to watch the movie or not. This paper examines the influence of Douban's movie reviews and ratings on Chinese movie-watching choices through the use of questionnaires in the context of the media environment and media psychology, based on Douban's unique media culture. The results of the questionnaire show that in a film market surrounded by all kinds of information, Douban movies largely satisfy the needs and psychology of users' viewing guides, and in the eyes of a large proportion of people Douban's ratings and reviews have become the main measure of whether viewers will watch them or not.

The Reception of Wuthering Heights in China: English-Chinese Translation, Dissemination, and Adaptation

Emily Brontë’s Wuthering Heights has been translated, disseminated, and adapted for various Chinese audiences. This study employed historical and archival research methods to probe this phenomenon. We retrieved data from Wuthering Heights and its Chinese-translated versions, children’s literature works, monographs, academic papers from China National Knowledge Infrastructure, and comments from the Douban Movie website. First, the researchers investigated Wuthering Heights’ translation in China from monographs and academic papers to explore the features of representative Chinese-translated versions. Second, the researchers explored Wuthering Heights’ dissemination in foreign literature education and literary study in China by analyzing the literature textbooks and academic papers. Third, the researchers examined the features of children’s literature works adapted from Wuthering Heights and the comments viewers made on the films adapted from the novel. The researchers found that the past nine decades witnessed the successful reception of Wuthering Heights in China for four reasons. First, the policies in the literary field contributed to its translation. Second, the reviews of the work in British literature textbooks and academic studies promoted the original text’s dissemination. Third, disseminating the movie adaptations enables more Chinese readers to know the classic, contributing to the original text’s dissemination. Fourth, the transmission of the children’s extracurricular bilingual reading material adapted from it gave Chinese teenagers a chance to know the novel’s plot and enhanced their reading ability, promoting the dissemination of Wuthering Heights in China.

Improving the recommendation accuracy of TrustSVD via trustworthy analysis in the social network environment

Recommender systems help Internet users quickly find information they may be interested in from an extremely large amount of resources. Recent studies have shown that incorporating auxiliary social trust relationship information into the recommender system improves the accuracy of recommendations. Most existing research only considers explicit trust relationships, which result in sub-optimal recommendation performance. In this research, we present a trust model which analyses user trustworthiness based on user’s behaviours on the social networks. The proposed trust model increases the density of trust relationships by considering explicit and implicit social trust relationships and also reflects a more fine-grained and realistic trust level between users. This improved social trust information is then incorporated into TrustSVD, a matrix factorisation–based social recommendation method. By analysing the prediction result using a real-world data set, Douban-600k from the Douban Movie website, we found that our proposed method provides more accurate predictions compared with SVD++ and traditional TrustSVD, improving users’ experiences.

An Augmented Neural Network for Sentiment Analysis Using Grammar.

Understanding human sentiment from their expressions is very important in human-robot interaction. But deep learning models are hard to represent grammatical changes for natural language processing (NLP), especially for sentimental analysis, which influence the robot's judgment of sentiment. This paper proposed a novel sentimental analysis model named MoLeSy, which is an augmentation of neural networks incorporating morphological, lexical, and syntactic knowledge. This model is constructed from three concurrently processed classical neural networks, in which output vectors are concatenated and reduced with a single dense neural network layer. The models used in the three grammatical channels are convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected dense neural networks. The corresponding output in the three channels is morphological, lexical, and syntactic results, respectively. Experiments are conducted on four different sentimental analysis corpuses, namely, hotel, NLPCC2014, Douban movie reviews dataset, and Weibo. MoLeSy can achieve the best performance over previous state-of-art models. It indicated that morphological, lexical, and syntactic grammar can augment the neural networks for sentimental analysis.

Fusing User Reviews Into Heterogeneous Information Network Recommendation Model

With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online. As an effective method to deal with this dilemma of information overload, recommendation system has become a popular area of research for the past few years. However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model. We try to combine heterogeneous information network with deep learning technology to improve recommendation performance. The working mechanism is to represent the rich auxiliary data by using heterogeneous information network, learning the features of entities through network embedding; in the meantime, by adopting deep learning technology to mine the features of entities from review texts, using the attention mechanism to fuse feature vectors of entities, inputting them into the neural network for recommendation; the last step is that we use Yelp dataset and Douban movie dataset to verify the FHRec model. The experimental results show that the FHRec behaves better than the traditional comparison algorithm on the Yelp dataset and Douban movie dataset.

Visual Analysis and Interactive Comparison for Heterogeneous Information Network Embedding Model

<p indent="0mm">To understand the differences between various heterogeneous information networks (HINs) embedding models, and to solve intricate and hidden problems in model evaluation, the comparative analysis method should include the following steps. First, the evaluation indicators and tasks are made consistent for all models. Then the parameters and structural features are extracted from the model training process and the complete and non-averaged evaluation results are retained for visualization. Based on the model parameters and characteristics data, we design and implement a visual analysis tool named HINCompare, which includes an overview of the distribution of basic evaluation indicators, a comparison view of recommended results, and a feature view of the local topology structure for model embedding. The tool allows developers to explore the common patterns of different feature aggregation methods of different models and the differences in their architectures. In addition, HINCompare shows user’s preferences for movie types and years with heat maps, which are combined with the contextual information of the recommended results for further analysis and evaluation. The system provides insights into black-box problems of models and increases interpretability by supplying information about the sources of the results. We conduct the preliminary evaluation study with Douban movie data to verify the effectiveness of the system.

Exploiting Group Information for Personalized Recommendation with Graph Neural Networks

Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared

Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

A Nonlinear Feature Fusion-Based Rating Prediction Algorithm in Heterogeneous Network

Due to the flexibility of heterogeneous information networks (HINs) in modeling heterogeneous data, researchers begin to use it to integrate the objects and relationships in recommender systems. However, how to abstract and exploit effective information and apply the information to recommender systems is a challenge. To fully mine nodes' structural features and better integrate these features simultaneously, we present a nonlinear feature fusion-based rating prediction algorithm. This algorithm first uses a meta-path-based HIN embedding model to extract the nodes' structural features. Then, the structural features are converted by a nonlinear fusion method. Finally, the fused features are input into the multilayer perceptron to achieve rating prediction. Experiments on real-life data sets, such as Movielens-100k, Yelp, Douban Book, and Douban Movie, are designed to prove the performance of the proposed model. Experimental results on the four data sets reveal that our algorithm is superior to the baselines.

Generating Knowledge-Based Attentive User Representations for Sparse Interaction Recommendation

Deep neural networks (DNNs) have been widely imported into collaborative-filtering (CF) based recommender systems and yielded remarkable superiority over traditional recommendation models. However, most deep CF-based models perform weakly when observed user-item interactions are sparse since user preferences and item characteristics are inferred mainly based on observed (historical) interactions. To address this problem, we propose a deep knowledge-enhanced recommendation model in this paper. Specifically, to augment user/item representations in the scenario of sparse historical user-item interactions, we first incorporate the knowledge from open knowledge graphs and personal information of users as side information, from which sufficient features of users and items are extracted. Second, to well capture shifted user preferences, we leverage a memory component constituted by recently interacted items rather than all historical ones. Third, attentive user representations are generated by attention mechanism to capture the diversity of user preferences. Furthermore, we build a convolutional neural network to pool the latent features in user representations for better user modeling, which enhances recommendation performance further. Our extensive experiments conducted against two real-world datasets, i.e., Douban movie and NetEase music, demonstrate our model’s remarkable superiority over the state-of-the-art deep recommendation models.

A Tri-Attention Neural Network Model-BasedRecommendation

Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems. Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored. To solve the above deficiencies, we propose a tri-attention neural network (TANN) model for recommendation task. The proposed TANN model applies the stud genetic algorithm to automatically select meta-paths at first. Then, it learns global and local representations of each node, as well as the representations of meta-paths existing in HIN. After that, a tri-attention mechanism is proposed to enhance the mutual influence among users, items, and their related meta-paths. Finally, the encoded interaction information among the user, the item, and their related meta-paths, which contain more semantic information can be used for recommendation task. Extensive experiments on the Douban Movie, MovieLens, and Yelp datasets have demonstrated the outstanding performance of the proposed approach.

Proposed Collaborative Filtering Recommender System Based on Implicit and Explicit User's Preferences

The expansion of web applications like e-commerce and other services yields an exponential increase in offers and choices in the web. From these needs, the recommender system applications have arisen. This research proposed a recommender system that uses user's reviews as implicit feedback to extract user preferences from their reviews to enhance personalization in addition to the explicit ratings. Diversity also improved by using k-furthest neighbor algorithm upon user's clusters. The system tested using Douban movie standard dataset from Kaggle, and show good performance.

Learning social regularized user representation in recommender system

Appropriate user and item representation learning is critical to a successful recommender system. A set of models from natural language processing domain, namely neural language models, have recently been utilized to user and item representation learning in standard recommendation tasks. This paper proposes to exploit neural language models in the social recommendation task. Unlike previous studies that focus on modeling the user-item interaction matrix and only consider the item-level context, this paper models user social relationship information and adds an additional layer to incorporate user-level context. The derived representation is very like the social regularization imposed in matrix factorization-based recommendation, but with more flexible context. Experiments on a Douban movie dataset validate the advantage of the proposed model.

Chinese Netizens’ Reactions to Red Classics Cinema Animation A Case Study of Taking Tiger Mountain by Strategy (2011)

&lt;p class="1Body"&gt;‘Red Classics’ can be considered as an important subgenre of Chinese cinema animation, which is often adapted from war novels, revolutionary model opera, songs and live-action films&lt;em&gt;. &lt;/em&gt;Animated film &lt;em&gt;Taking Tiger Mountain by Strategy (2011)&lt;/em&gt;, as a Red Classics cinema animation production, has provoked heated discussion among Chinese netizens in two largest online film communities and review aggregator sites &lt;em&gt;Douban Movie &lt;/em&gt;and &lt;em&gt;Mtime&lt;/em&gt;. This paper reveals the Chinese netizens’ reception process of this movie before and after public screening by analysing the relevant brief comments and longer film reviews from the above two websites. Chinese audiences’ expectation on &lt;em&gt;Taking Tiger Mountain by Strategy&lt;/em&gt; was relatively negative before exhibition in cinema. This can be partly attributed to the emotional discontent over the previous Chinese animation and government intervention on artistic creation. However, there has been a gradual shift in the attitudes of netizens towards this movie after the public exhibition, the reviews have become more objective, rational and positive. This article aims to identify the reasons behind such attitude change by applying reception studies approach, and how and to what extent these elements have affected Chinese netizens’ evaluation process.&lt;/p&gt;

User-Service Rating Prediction by Exploring Social Users' Rating Behaviors

With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users' rating behaviors. In order to predict user-service ratings, we focus on users' rating behaviors. In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user's rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users' daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users' rating behaviors. In the proposed user-service rating prediction approach, we fuse four factors-user personal interest (related to user and the item's topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users' rating behavior habits), and interpersonal rating behavior diffusion (related to users' behavior diffusions)-into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach.

Service Quality Evaluation by Exploring Social Users’ Contextual Information

Nowadays, with the boom of social media and e-commerce, more and more people prefer to share their consumption experiences and rate services on review sites. Much research has focused on personalized recommendation. However, quality of service also plays an important role in recommender systems, and it is the main concern of this paper. An overall rating that indicates the popular view usually represents the evaluation. There are some challenges when we do not have enough review information to extract public opinion. Take, for example, a movie for which one user rates a two star rating, and another rates a five star rating. In this case, it is difficult to conduct a quality evaluation fairly. However, it is possible to be improved with the help of big social users’ contextual information. In this paper, we propose a model to conduct service quality evaluation by improving overall rating of services using an empirical methodology. We use the concept of user rating's confidence, which denotes the trustworthiness of user ratings. First, entropy is utilized to calculate user ratings’ confidence. Second, we further explore spatial-temporal features and review sentimental features of user ratings to constrain their confidences. Last, we fuse them into a unified model to calculate an overall confidence, which is utilized to perform service quality evaluation. Extensive experiments implemented on Yelp and Douban Movie datasets demonstrate the effectiveness of our model.