Recommender systems play an important role in helping online users find relevant information by suggesting information of potential interest to them Due to the potential value of social relations in recommender systems, social recommendation has attracted increasing attention in recent years In this paper, we present a review of existing recommender systems and discuss some research directions We begin by giving formal definitions of social recommendation and discuss the unique property of social recommendation and its implications compared with those of traditional recommender systems Then, we classify existing social recommender systems into memory-based social recommender systems and model-based social recommender systems, according to the basic models adopted to build the systems, and review representative systems for each category We also present some key findings from both positive and negative experiences in building social recommender systems, and research directions to improve social recommendation capabilities

Social recommendation: a review

More often than not, people are active in more than one social network. Identifying users from multiple heterogeneous social networks and integrating the different networks is a fundamental issue in many applications. The existing methods tackle this problem by estimating pairwise similarity between users in two networks. However, those methods suffer from potential inconsistency of matchings between multiple networks.In this paper, we propose COSNET (COnnecting heterogeneous Social NETworks with local and global consistency), a novel energy-based model, to address this problem by considering both local and global consistency among multiple networks. An efficient subgradient algorithm is developed to train the model by converting the original energy-based objective function into its dual form.We evaluate the proposed model on two different genres of data collections: SNS and Academia, each consisting of multiple heterogeneous social networks. Our experimental results validate the effectiveness and efficiency of the proposed model. On both data collections, the proposed COSNET method significantly outperforms several alternative methods by up to 10-30% (p

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COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency

We study the problem of large-scale social identity linkage across different social media platforms, which is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. This paper proposes HYDRA, a solution framework which consists of three key steps: (I) modeling heterogeneous behavior by long-term behavior distribution analysis and multi-resolution temporal information matching; (II) constructing structural consistency graph to measure the high-order structure consistency on users' core social structures across different platforms; and (III) learning the mapping function by multi-objective optimization composed of both the supervised learning on pair-wise ID linkage information and the cross-platform structure consistency maximization. Extensive experiments on 10 million users across seven popular social network platforms demonstrate that HYDRA correctly identifies real user linkage across different platforms, and outperforms existing state-of-the-art algorithms by at least 20% under different settings, and 4 times better in most settings.

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HYDRA: large-scale social identity linkage via heterogeneous behavior modeling

The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.

User Identity Linkage across Online Social Networks: A Review

In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/ followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with "similar" followers/followees in the embedded space. For the alignment, we add both known and potential anchor users across the networks to facilitate the transfer of context information across networks. We solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. The stochastic gradient descent and negative sampling algorithms are used to address scalability issues. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.

Aligning users across social networks using network embedding

In this paper, we consider the problem of linking users across multiple online communities. Specifically, we focus on the alias-disambiguation step of this user linking task, which is meant to differentiate users with the same usernames. We start quantitatively analyzing the importance of the alias-disambiguation step by conducting a survey on 153 volunteers and an experimental analysis on a large dataset of About.me (75,472 users). The analysis shows that the alias-disambiguation solution can address a major part of the user linking problem in terms of the coverage of true pairwise decisions (46.8%). To the best of our knowledge, this is the first study on human behaviors with regards to the usages of online usernames. We then cast the alias-disambiguation step as a pairwise classification problem and propose a novel unsupervised approach. The key idea of our approach is to automatically label training instances based on two observations: (a) rare usernames are likely owned by a single natural person, e.g. pennystar88 as a positive instance; (b) common usernames are likely owned by different natural persons, e.g. tank as a negative instance. We propose using the n-gram probabilities of usernames to estimate the rareness or commonness of usernames. Moreover, these two observations are verified by using the dataset of Yahoo! Answers. The empirical evaluations on 53 forums verify: (a) the effectiveness of the classifiers with the automatically generated training data and (b) that the rareness and commonness of usernames can help user linking. We also analyze the cases where the classifiers fail.

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What's in a name?: an unsupervised approach to link users across communities

This paper focuses on mining the hyponymy (or is-a) relation from large-scale, open-domain web documents. A nonlinear probabilistic model is exploited to model the correlation between sentences in the aggregation of pattern matching results. Based on the model, we design a set of evidence combination and propagation algorithms. These significantly improve the result quality of existing approaches. Experimental results conducted on 500 million web pages and hypernym labels for 300 terms show over 20% performance improvement in terms of P@5, MAP and R-Precision.

Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining

Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals obtaining the information they require efficiently. At the same time, the prevalence of popularity bias in such systems has become a widely acknowledged issue. To address this challenge, we propose a novel method named Model-Agnostic Popularity Debias Training Framework (MDTF). It consists of two basic modules including 1) General Ranking Model (GRM), which is model-agnostic and can be implemented as any ranking models; and 2) Popularity Debias Module (PDM), which estimates the impact of the competitiveness and popularity of candidate items on the CTR, by utilizing the feedback of cold-start users to re-weigh the loss in GRM. MDTF seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.

A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System

Online social media platforms are becoming increasingly popular.A huge number of people are using such platforms to share various information. Supply and demand constitute two important kinds of information in social media platforms. Examples are sell and buy, positions and job seeking, houses for rent and needs of accommodation,and so on.Consequently, it is relevant to match such supply and demand information automatically in order to facilitate the trades online. However, the problem is challenging due to the high volumes of information expressed in short and vague texts. In this paper, we propose a complete set of techniques to resolve the problem. First, we design an intention recognition model to recognize a user’ s complete intention from her/his original text posts,The recognized intention includes expected behaviors, intention objects, user sentiments and other relevant constraints. Second, we define the similarity for two intentions that are recognized. Third, we devise an algorithm to match supply and demand intentions whose similarity is sufficiently high. We evaluate our proposal on real data. The results show that our approach returns accurate matching results efficiently.

Fan Zhang

Papers

What's in a name?: an unsupervised approach to link users across communities

Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining

A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System

Multi-Dimensional Intention Recognition for User-Generated Short Texts