Sentiment-guided Sequential Recommendation

Zheng, Lin; Guo, Naicheng; Wei-hao, Chen; Yu, Jin; Jiang, Dazhi

doi:10.1145/3397271.3401330

Cited by 28 publications

(10 citation statements)

References 6 publications

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“…The emotional attributes are mainly based on emotional analysis of text information. In [27], Zheng et al proposed a new preference prediction mechanism from the perspective of users' subjective emotions, taking into account the influence of changes in user emotions over time on user behavior in sequence recommendation. In addition, Zhang et al [28] proposed a method of prefiltering opinions using a comprehensive measurement of a user's emotional orientation and original rating level.…”

Section: Sentiment Analysis and Its Application Inmentioning

confidence: 99%

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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To comply with the rapid development of big data in mobile services, an increasing number of websites have begun to provide users with recommendation decisions in various areas, like shopping, tourism, food, and medical treatment. However, there are still some challenges in the field of medical recommendation systems, such as the lack of personalized medical recommendations and the problem of data sparseness, which seriously restricts the effectiveness of such recommendations. In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&R-CNN). First, the review text is divided into user and doctor datasets, and BERT vectorized representations are performed on them. Moreover, the original rating features are revised by adding the sentiment analysis values of the review text. Then, the vectorized review text and the revised rating features are spliced together and input into the convolutional neural network to extract the deep nonlinear feature vectors of both users and doctors. Finally, we use a factorization machine for feature interaction. We conduct comparison experiments based on a Yelp dataset in the “Health & Medical” category. The experimental results confirm the conclusion that RR&R-CNN has a better effect compared to a traditional method.

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Section: Sentiment Analysis and Its Application Inmentioning

confidence: 99%

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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“…After the preference identification module, we get the representation for the multiple preference. Different from existing methods that use item representations to predict the next item [13,18,39,68], we use the learned multiple preference representation to do recommendation:…”

Section: Mrtransformermentioning

confidence: 99%

Improving Transformer-based Sequential Recommenders through Preference Editing

Ma¹,

Ren²,

Chen³

et al. 2021

Preprint

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One of the key challenges in sequential recommendation (SR) is how to extract and represent user preferences. Traditional SR methods rely on the next item as the supervision signal to guide preference extraction and representation. We propose a novel learning strategy, named preference editing. The idea is to force the SR model to discriminate the common and unique preferences in different sequences of interactions between users and the recommender system. By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation. We propose a transformer based SR model, named MrTransformer (Multi-preference Transformer), that concatenates some special tokens in front of the sequence to represent multiple user preferences and makes sure they capture different aspects through a preference coverage mechanism. Then, we devise a preference editing-based self-supervised learning mechanism for training MrTransformer that contains two main operations: preference separation and preference recombination. The former separates the common and unique user preferences for a given pair of sequences. The latter swaps the common preferences to obtain recombined user preferences for each sequence. Based on the preference separation and preference recombination operations, we define two types of self-supervised learning loss that require that the recombined preferences are similar to the original ones, and that the common preferences are close to each other.We carry out extensive experiments on two benchmark datasets. MrTransformer with preference editing significantly outperforms state-of-the-art SR methods in terms of Recall, MRR and NDCG. We find that long sequences whose user preferences are harder to extract and represent benefit most from preference editing.

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“…For example, using text sentiment classification technique, mine user opinion information from UGC to improve matrix factorization recommendations [48][49][50], collaborative filtering recommendations [51], hybrid recommendations [52], sequential recommendations [53], and crossdomain recommendations [54].…”

Section: Researches On Customer Needs Miningmentioning

confidence: 99%

Implicit Feedback Recommendation Method Based on User-Generated Content

Fang

Shen

et al. 2021

Scientific Programming

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Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

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Sentiment-guided Sequential Recommendation

Cited by 28 publications

References 6 publications

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

Improving Transformer-based Sequential Recommenders through Preference Editing

Implicit Feedback Recommendation Method Based on User-Generated Content

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