A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

Cheng, Zhiyong; Ding, Ying; He, Xiangnan; Zhu, Lei; Song, Xuemeng; Kankanhalli, Mohan

doi:10.24963/ijcai.2018/521

Cited by 173 publications

(147 citation statements)

References 5 publications

Supporting

Mentioning

145

Contrasting

Order By: Relevance

“…However, the aforementioned approaches have not taken personalization into consideration, yet it is vital for the product search. In fact, deep learning has been successfully applied in recommender systems to model the user preference [9,25,35]. In particular, the attention mechanism is usually adopted in these systems to model the user's preference more accurately.…”

Section: Deep Learning In Information Retrievalmentioning

confidence: 99%

Attentive Long Short-Term Preference Modeling for Personalized Product Search

Guo

Cheng

Nie

et al. 2019

ACM Trans. Inf. Syst.

Self Cite

View full text Add to dashboard Cite

E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-term ones and short-term ones. The former refers to users' inherent purchasing bias and evolves slowly. By contrast, the latter reflects users' purchasing inclination in a relatively short period. They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long-and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users' current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long-and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.

show abstract

Section: Deep Learning In Information Retrievalmentioning

confidence: 99%

Attentive Long Short-Term Preference Modeling for Personalized Product Search

Guo

Cheng

Nie

et al. 2019

ACM Trans. Inf. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…where r ∈ R da is the query vector. Similarly, in Equations (4), (5), and (6), µ (u) , r, W r ∈ R da×dr , and b r ∈ R da will also be learned during the model learning.…”

Section: B Personalized Embeddingmentioning

confidence: 99%

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

Luo

Yang

2019

2019 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at https://github.com/lola63/HDE-Python.

show abstract

“…In addition, the side information of items is exploited to estimate the weight vector, as side information conveys rich features of items, especially text reviews and item images, which are wellrecognized to provide notable and complementary features of items in different aspects [9,54]. We adopt the recent advancement of attention mechanism [6,10] to estimate the attention vector. With the attention (weight) vector, the Euclidean distance between a user u and an item i in our model becomes:…”

Section: Multimodal Attentive Metric Learningmentioning

confidence: 99%

“…Recently, some researchers have noticed this problem and proposed several models to tackle it. One type of methods leverages reviews to analyze user attention on different aspects of items and then integrates the results into the matrix factorization methods for recommendation, such as ALFM [11], A 3 NCF [10], and ANR [12]. Another type of methods varies the target item or user vector based on the vectors of those most influential items or users with respect to the target items.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 27th ACM International Conference on Multimedia

2019

View full text Add to dashboard Cite

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploits the item's multimodal features to estimate the user's special attention to different aspects of this item. The obtained attention is then integrated into a metric-based learning method to predict the user preference on this item. The advantage of metric learning is that it can naturally overcome the problem of dot product similarity, which is adopted by matrix factorization (MF) based recommendation models but does not satisfy the triangle inequality property. In addition, it is worth mentioning that the attention mechanism cannot only help model user's diverse preferences towards different items, but also overcome the geometrically restrictive problem caused by collaborative metric learning. Extensive experiments on large-scale real-world datasets show that our model can substantially outperform the state-of-the-art baselines, demonstrating the potential of modeling user diverse preference for recommendation.

show abstract

A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

Cited by 173 publications

References 5 publications

Attentive Long Short-Term Preference Modeling for Personalized Product Search

Attentive Long Short-Term Preference Modeling for Personalized Product Search

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

Proceedings of the 27th ACM International Conference on Multimedia

Contact Info

Product

Resources

About