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"Recommendation": models, code, and papers

Req2Lib: A Semantic Neural Model for Software Library Recommendation

May 24, 2020
Zhensu Sun, Yan Liu, Ziming Cheng, Chen Yang, Pengyu Che

Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.

* 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) 
* 5 pages 

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A Fast Matrix-Completion-Based Approach for Recommendation Systems

Dec 04, 2019
Meng Qiao, Zheng Shan, Fudong Liu, Wenjie Sun

Matrix completion is widely used in machine learning, engineering control, image processing, and recommendation systems. Currently, a popular algorithm for matrix completion is Singular Value Threshold (SVT). In this algorithm, the singular value threshold should be set first. However, in a recommendation system, the dimension of the preference matrix keeps changing. Therefore, it is difficult to directly apply SVT. In addition, what the users of a recommendation system need is a sequence of personalized recommended results rather than the estimation of their scores. According to the above ideas, this paper proposes a novel approach named probability completion model~(PCM). By reducing the data dimension, the transitivity of the similar matrix, and singular value decomposition, this approach quickly obtains a completion matrix with the same probability distribution as the original matrix. The approach greatly reduces the computation time based on the accuracy of the sacrifice part, and can quickly obtain a low-rank similarity matrix with data trend approximation properties. The experimental results show that PCM can quickly generate a complementary matrix with similar data trends as the original matrix. The LCS score and efficiency of PCM are both higher than SVT.

* 16 pages, 5 figures 

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Modeling the Past and Future Contexts for Session-based Recommendation

Jun 12, 2019
Yuan Fajie, He Xiangnan, Guo Guibing, Xu Zhezhao, Xiong Jian, He Xiuqiang

Long session-based recommender systems have attacted much attention recently. For each user, they may create hundreds of click behaviors in short time. To learn long session item dependencies, previous sequential recommendation models resort either to data augmentation or a left-to-right autoregressive training approach. While effective, an obvious drawback is that future user behaviors are always mising during training. In this paper, we claim that users' future action signals can be exploited to boost the recommendation quality. To model both past and future contexts, we investigate three ways of augmentation techniques from both data and model perspectives. Moreover, we carefully design two general neural network architectures: a pretrained two-way neural network model and a deep contextualized model trained on a text gap-filling task. Experiments on four real-word datasets show that our proposed two-way neural network models can achieve competitive or even much better results. Empirical evidence confirms that modeling both past and future context is a promising way to offer better recommendation accuracy.


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NPA: Neural News Recommendation with Personalized Attention

Jul 12, 2019
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.


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TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation

Jan 27, 2021
Guangneng Hu, Qiang Yang

We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news). To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus. To tackle the heterogeneity of different user interests and of different word distributions across corpora, we design a translator-based transfer-learning strategy to learn a representation mapping between source and target corpora. The learned translator can be used to generate representations for unseen users in the future. We show through experiments on real-world datasets that TrNews is better than various baselines in terms of four metrics. We also show that our translator is effective among existing transfer strategies.

* EACL 2021 

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Trust-aware Collaborative Denoising Auto-Encoder for Top-N Recommendation

May 08, 2017
Yiteng Pan, Fazhi He, Haiping Yu

Both feedback of ratings and trust relationships can be used to reveal users' tastes for improving recommendation performance, especially for cold users. However, both of them are facing data sparsity problem, which may severely degrade recommendation performance. In this paper, we propose to utilize the idea of Denoising Auto-Encoders (DAE) to tackle this problem. Specially, we propose a novel deep learning model, the \textit{Trust-aware Collaborative Denoising Auto-Encoder} (TDAE), to learn compact and effective representations from both rating and trust data for top-N recommendation. In particular, we present a novel neutral network with a weighted hidden layer to balance the importance of these representations. Moreover, we propose a novel correlative regularization to bridge relations between user preferences in different perspectives. We also conduct comprehensive experiments on two public datasets to compare with several state-of-the-art approaches. The results demonstrate that the proposed method significantly outperforms other comparisons for top-N recommendation task.

* Submitted to a journal 

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Learning Complex Users' Preferences for Recommender Systems

Jul 04, 2021
Shahpar Yakhchi

Recommender systems (RSs) have emerged as very useful tools to help customers with their decision-making process, find items of their interest, and alleviate the information overload problem. There are two different lines of approaches in RSs: (1) general recommenders with the main goal of discovering long-term users' preferences, and (2) sequential recommenders with the main focus of capturing short-term users' preferences in a session of user-item interaction (here, a session refers to a record of purchasing multiple items in one shopping event). While considering short-term users' preferences may satisfy their current needs and interests, long-term users' preferences provide users with the items that they may interact with, eventually. In this thesis, we first focus on improving the performance of general RSs. Most of the existing general RSs tend to exploit the users' rating patterns on common items to detect similar users. The data sparsity problem (i.e. the lack of available information) is one of the major challenges for the current general RSs, and they may fail to have any recommendations when there are no common items of interest among users. We call this problem data sparsity with no feedback on common items (DSW-n-FCI). To overcome this problem, we propose a personality-based RS in which similar users are identified based on the similarity of their personality traits.

* 269 pages, 43 figures, 26 tables 

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Learning Item-Interaction Embeddings for User Recommendations

Dec 11, 2018
Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong

Industry-scale recommendation systems have become a cornerstone of the e-commerce shopping experience. For Etsy, an online marketplace with over 50 million handmade and vintage items, users come to rely on personalized recommendations to surface relevant items from its massive inventory. One hallmark of Etsy's shopping experience is the multitude of ways in which a user can interact with an item they are interested in: they can view it, favorite it, add it to a collection, add it to cart, purchase it, etc. We hypothesize that the different ways in which a user interacts with an item indicates different kinds of intent. Consequently, a user's recommendations should be based not only on the item from their past activity, but also the way in which they interacted with that item. In this paper, we propose a novel method for learning interaction-based item embeddings that encode the co-occurrence patterns of not only the item itself, but also the interaction type. The learned embeddings give us a convenient way of approximating the likelihood that one item-interaction pair would co-occur with another by way of a simple inner product. Because of its computational efficiency, our model lends itself naturally as a candidate set selection method, and we evaluate it as such in an industry-scale recommendation system that serves live traffic on Etsy.com. Our experiments reveal that taking interaction type into account shows promising results in improving the accuracy of modeling user shopping behavior.


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