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"Recommendation": models, code, and papers

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Jan 30, 2013
John S. Breese, David Heckerman, Carl Kadie

Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.

* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 

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A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising

Feb 18, 2021
Shiyao Wang, Qi Liu, Tiezheng Ge, Defu Lian, Zhiqiang Zhang

Creative plays a great important role in e-commerce for exhibiting products. Sellers usually create multiple creatives for comprehensive demonstrations, thus it is crucial to display the most appealing design to maximize the Click-Through Rate~(CTR). For this purpose, modern recommender systems dynamically rank creatives when a product is proposed for a user. However, this task suffers more cold-start problem than conventional products recommendation In this paper, we propose a hybrid bandit model with visual priors which first makes predictions with a visual evaluation, and then naturally evolves to focus on the specialities through the hybrid bandit model. Our contributions are three-fold: 1) We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. 2) Regarding visual evaluations as a prior, the hybrid bandit model (called HBM) is proposed to evolve consistently to make better posteriori estimations by taking more observations into consideration for online scenarios. 3) A first large-scale creative dataset, CreativeRanking, is constructed, which contains over 1.7M creatives of 500k products as well as their real impression and click data. Extensive experiments have also been conducted on both our dataset and public Mushroom dataset, demonstrating the effectiveness of the proposed method.

* To be published in the International World Wide Web Conference (WWW) 2021 

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Graph Data on the Web: extend the pivot, don't reinvent the wheel

Mar 11, 2019
Fabien Gandon, Franck Michel, Olivier Corby, Michel Buffa, Andrea Tettamanzi, Catherine Faron Zucker, Elena Cabrio, Serena Villata

This article is a collective position paper from the Wimmics research team, expressing our vision of how Web graph data technologies should evolve in the future in order to ensure a high-level of interoperability between the many types of applications that produce and consume graph data. Wimmics stands for Web-Instrumented Man-Machine Interactions, Communities, and Semantics. We are a joint research team between INRIA Sophia Antipolis-M{\'e}diterran{\'e}e and I3S (CNRS and Universit{\'e} C{\^o}te d'Azur). Our challenge is to bridge formal semantics and social semantics on the web. Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources. In this position paper, we emphasize the need to extend the semantic Web standard stack to address and fulfill new graph data needs, as well as the importance of remaining compatible with existing recommendations, in particular the RDF stack, to avoid the painful duplication of models, languages, frameworks, etc. The following sections group motivations for different directions of work and collect reasons for the creation of a working group on RDF 2.0 and other recommendations of the RDF family.

* W3C Workshop on Web Standardization for Graph Data - Creating Bridges: RDF, Property Graph and SQL, Mar 2019, Berlin, France. 2019, https://www.w3.org/Data/events/data-ws-2019/index.html 

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Clustering on Multiple Incomplete Datasets via Collective Kernel Learning

May 06, 2016
Weixiang Shao, Xiaoxiao Shi, Philip S. Yu

Multiple datasets containing different types of features may be available for a given task. For instance, users' profiles can be used to group users for recommendation systems. In addition, a model can also use users' historical behaviors and credit history to group users. Each dataset contains different information and suffices for learning. A number of clustering algorithms on multiple datasets were proposed during the past few years. These algorithms assume that at least one dataset is complete. So far as we know, all the previous methods will not be applicable if there is no complete dataset available. However, in reality, there are many situations where no dataset is complete. As in building a recommendation system, some new users may not have a profile or historical behaviors, while some may not have a credit history. Hence, no available dataset is complete. In order to solve this problem, we propose an approach called Collective Kernel Learning to infer hidden sample similarity from multiple incomplete datasets. The idea is to collectively completes the kernel matrices of incomplete datasets by optimizing the alignment of the shared instances of the datasets. Furthermore, a clustering algorithm is proposed based on the kernel matrix. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The proposed clustering algorithm outperforms the comparison algorithms by as much as two times in normalized mutual information.

* ICDM 2013, Code available at https://github.com/software-shao/Collective-Kernel-Learning 

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ECLARE: Extreme Classification with Label Graph Correlations

Jul 31, 2021
Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, Manik Varma

Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels that are rarely seen during training. Such rare labels hold the key to personalized recommendations that can delight and surprise a user. However, the large number of rare labels and small amount of training data per rare label offer significant statistical and computational challenges. State-of-the-art deep XC methods attempt to remedy this by incorporating textual descriptions of labels but do not adequately address the problem. This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds. Core contributions of ECLARE include a frugal architecture and scalable techniques to train deep models along with label correlation graphs at the scale of millions of labels. In particular, ECLARE offers predictions that are 2 to 14% more accurate on both publicly available benchmark datasets as well as proprietary datasets for a related products recommendation task sourced from the Bing search engine. Code for ECLARE is available at https://github.com/Extreme-classification/ECLARE.

* The Web Conference 2021 

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Improving Next-Application Prediction with Deep Personalized-Attention Neural Network

Nov 09, 2021
Jun Zhu, Gautier Viaud, Céline Hudelot

Recently, due to the ubiquity and supremacy of E-recruitment platforms, job recommender systems have been largely studied. In this paper, we tackle the next job application problem, which has many practical applications. In particular, we propose to leverage next-item recommendation approaches to consider better the job seeker's career preference to discover the next relevant job postings (referred to jobs for short) they might apply for. Our proposed model, named Personalized-Attention Next-Application Prediction (PANAP), is composed of three modules. The first module learns job representations from textual content and metadata attributes in an unsupervised way. The second module learns job seeker representations. It includes a personalized-attention mechanism that can adapt the importance of each job in the learned career preference representation to the specific job seeker's profile. The attention mechanism also brings some interpretability to learned representations. Then, the third module models the Next-Application Prediction task as a top-K search process based on the similarity of representations. In addition, the geographic location is an essential factor that affects the preferences of job seekers in the recruitment domain. Therefore, we explore the influence of geographic location on the model performance from the perspective of negative sampling strategies. Experiments on the public CareerBuilder12 dataset show the interest in our approach.


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DECAF: Deep Extreme Classification with Label Features

Aug 01, 2021
Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal, Deepak Saini, Sumeet Agarwal, Purushottam Kar, Manik Varma

Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with millions of products. Although leading XML algorithms scale to millions of labels, they largely ignore label meta-data such as textual descriptions of the labels. On the other hand, classical techniques that can utilize label metadata via representation learning using deep networks struggle in extreme settings. This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels. DECAF makes specific contributions to model architecture design, initialization, and training, enabling it to offer up to 2-6% more accurate prediction than leading extreme classifiers on publicly available benchmark product-to-product recommendation datasets, such as LF-AmazonTitles-1.3M. At the same time, DECAF was found to be up to 22x faster at inference than leading deep extreme classifiers, which makes it suitable for real-time applications that require predictions within a few milliseconds. The code for DECAF is available at the following URL https://github.com/Extreme-classification/DECAF.

* Web Search and Data Mining 2021 

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BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

Mar 10, 2021
Chang-Dong Wang, Zi-Yuan Hu, Jin Huang, Zhi-Hong Deng, Ling Huang, Jian-Huang Lai, Philip S. Yu

Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning tries to learn a common low dimensional space for the representations of users and items. In this case, a user and item match better if they have higher similarity in that common space. Matching function learning tries to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the representation learning resorts to applying a dot product which has limited expressiveness on the latent features of users and items, while the matching function learning has weakness in capturing low-rank relations. To overcome such flaws, we propose a novel recommendation model named Balanced Collaborative Filtering Network (BCFNet), which has the strengths of the two types of methods. In addition, an attention mechanism is designed to better capture the hidden information within implicit feedback and strengthen the learning ability of the neural network. Furthermore, a balance module is designed to alleviate the over-fitting issue in DNNs. Extensive experiments on eight real-world datasets demonstrate the effectiveness of the proposed model.


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Hybrid Interest Modeling for Long-tailed Users

Dec 29, 2020
Lifang Deng, Jin Niu, Angulia Yang, Qidi Xu, Xiang Fu, Jiandong Zhang, Anxiang Zeng

User behavior modeling is a key technique for recommender systems. However, most methods focus on head users with large-scale interactions and hence suffer from data sparsity issues. Several solutions integrate side information such as demographic features and product reviews, another is to transfer knowledge from other rich data sources. We argue that current methods are limited by the strict privacy policy and have low scalability in real-world applications and few works consider the behavioral characteristics behind long-tailed users. In this work, we propose the Hybrid Interest Modeling (HIM) network to hybrid both personalized interest and semi-personalized interest in learning long-tailed users' preferences in the recommendation. To achieve this, we first design the User Behavior Pyramid (UBP) module to capture the fine-grained personalized interest of high confidence from sparse even noisy positive feedbacks. Moreover, the individual interaction is too sparse and not enough for modeling user interest adequately, we design the User Behavior Clustering (UBC) module to learn latent user interest groups with self-supervised learning mechanism novelly, which capture coarse-grained semi-personalized interest from group-item interaction data. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines.


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