Baoshi Yan

Palo Alto, California, United States Contact Info
1K followers 500+ connections

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Specialties: Large-scale, real time decision making leveraging AI/ML, Payment fraud…

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Publications

  • Generating Supplemental Content Information Using Virtual Profiles

    7th ACM conference on Recommender systems (RecSys 2013)

    We describe a hybrid recommendation system at LinkedIn that seeks to optimally extract relevant information pertaining to items to be recommended. By extending the notion of an item profile, we propose the concept of a "virtual profile" that augments the content of the item with rich set of features inherited from members who have already shown explicit interest in it. Unlike item-based collaborative filtering, we focus on discovering the characteristic descriptors that underlie the item-user…

    We describe a hybrid recommendation system at LinkedIn that seeks to optimally extract relevant information pertaining to items to be recommended. By extending the notion of an item profile, we propose the concept of a "virtual profile" that augments the content of the item with rich set of features inherited from members who have already shown explicit interest in it. Unlike item-based collaborative filtering, we focus on discovering the characteristic descriptors that underlie the item-user association. Such information is used as supplemental features in a content-based filtering system. The main objective of virtual profiles is to provide a means to tap into rich-content information from one type of entity and propagate features extracted from which to other affiliated entities that may suffer from relative data scarcity. We empirically evaluate the proposed method on a real-world community recommendation problem at LinkedIn. The result shows that the virtual profiles outperform a collaborative filtering based approach (user who likes this also likes that). In particular, the improvement is more significant for new users with only limited connections, demonstrating the capability of the method to address the cold-start problem in pure collaborative filtering systems.

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  • Pairwise Learning in Recommendation: Experiments with Pairwise Recommendation on LinkedIn

    7th ACM conference on Recommender systems (RecSys 2013)

    Many online systems present a list of recommendations and infer user interests implicitly from clicks or other contextual actions. For modeling user feedback in such settings, a common approach is to consider items acted upon to be relevant to the user, and irrelevant otherwise. However, clicking some but not others conveys an implicit ordering of the presented items. Pairwise learning, which leverages such implicit ordering between a pair of items, has been successful in areas such as search…

    Many online systems present a list of recommendations and infer user interests implicitly from clicks or other contextual actions. For modeling user feedback in such settings, a common approach is to consider items acted upon to be relevant to the user, and irrelevant otherwise. However, clicking some but not others conveys an implicit ordering of the presented items. Pairwise learning, which leverages such implicit ordering between a pair of items, has been successful in areas such as search ranking. In this work, we study whether pairwise learning can improve community recommendation. We first present two novel pairwise models adapted from logistic regression. Both offline and online experiments in a large real-world setting show that incorporating pairwise learning improves the recommendation performance. However, the improvement is only slight. We find that users' preferences regarding the kinds of communities they like can differ greatly, which adversely affect the effectiveness of features derived from pairwise comparisons. We therefore propose a probabilistic latent semantic indexing model for pairwise learning (Pairwise PLSI), which assumes a set of users' latent preferences between pairs of items. Our experiments show favorable results for the Pairwise PLSI model and point to the potential of using pairwise learning for community recommendation.

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  • Social Referral: Leveraging Network Connections to Deliver Recommendations

    the 6th ACM Conference on Recommender Systems (RecSys 2012), Dublin, 2012

    Much work has been done to study the interplay between recommender systems and social networks. This creates a very powerful coupling in presenting highly relevant recommendations to the users. However, to our knowledge, little attention has been paid to leverage a user’s social network to deliver these recommendations. We present a novel approach to aid delivery of recommendations using the recipient’s friends or connections. Our contributions with this study are 1) A novel recommendation…

    Much work has been done to study the interplay between recommender systems and social networks. This creates a very powerful coupling in presenting highly relevant recommendations to the users. However, to our knowledge, little attention has been paid to leverage a user’s social network to deliver these recommendations. We present a novel approach to aid delivery of recommendations using the recipient’s friends or connections. Our contributions with this study are 1) A novel recommendation delivery paradigm called Social Referral, which utilizes a user’s social network for the delivery of relevant content. 2) An implementation of the paradigm is described in a real industrial production setting of a large online professional network. 3) A study of the interaction between the trifecta of the recommender system, the trusted connections and the end consumer of the recommendation by comparing and contrasting the pro- posed approach’s performance with the direct recommender system.

    Our experiments indicate that Social Referral is a promising mechanism for recommendation delivery. The experiments show that a significant portion of users are recep- tive to passing along relevant recommendations to their so- cial networks, and that recommendations delivered through users’ social networks are much more likely to be accepted than those directly delivered to users.

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  • Entity Resolution Using Social Graphs for Business Applications

    The International Conference on Advances in Social Network Analysis and Mining (ASONAM 2011)

    Social network such as LinkedIn maintains profiles for its members in a semi-structured format. A lot of business applications like ad targeting and content recommendations rely on canonicalization of data elements like companies, titles and schools for enabling fine grained advertising or recommending candidates for job postings. In this paper we explore the issues around resolving company names for hundreds of millions of member positions to known company entities using the social graph. We…

    Social network such as LinkedIn maintains profiles for its members in a semi-structured format. A lot of business applications like ad targeting and content recommendations rely on canonicalization of data elements like companies, titles and schools for enabling fine grained advertising or recommending candidates for job postings. In this paper we explore the issues around resolving company names for hundreds of millions of member positions to known company entities using the social graph. We proposed a machine learning approach leveraging three dimensional feature sets including the social graph, social behavior and various content and demographic features. The experiments showed that our approach achieved high precision at a reasonable coverage and is significantly superior to a baseline content based approach.

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  • Contextual Recommendation based on Text Mining

    The 23rd International Conference on Computational Linguistics (COLING 2010)

    The potential benefit of integrating contextual information for recommendation
    has received much research attention recently, especially with the ever-increasing
    interest in mobile-based recommendation services. However, context based recommendation research is limited due to the lack of standard evaluation data with contextual information and reliable technology for extracting such information. As a result, there are no widely accepted conclusions on how, when and whether context helps…

    The potential benefit of integrating contextual information for recommendation
    has received much research attention recently, especially with the ever-increasing
    interest in mobile-based recommendation services. However, context based recommendation research is limited due to the lack of standard evaluation data with contextual information and reliable technology for extracting such information. As a result, there are no widely accepted conclusions on how, when and whether context helps. Additionally, a system often suffers from the so called cold start problem due to the lack of data for training the initial context based recommendation model. This paper proposes a novel solution to address these problems with
    automated information extraction techniques. We also compare several approaches for utilizing context based on a new data set collected using the proposed solution. The experimental results
    demonstrate that 1) IE-based techniques can help create a large scale context data
    with decent quality from online reviews, at least for restaurant recommendations; 2) context helps recommender systems rank items, however, does not help predict user ratings; 3) simply using context to filter items hurts recommendation performance, while a new probabilistic latent
    relational model we proposed helps.

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  • A Conversational In-Car Dialog System

    Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007)

    In this demonstration we present a conversational dialog system for automobile drivers. The system provides a voicebased interface to playing music, finding restaurants, and navigating while driving.
    The design of the system as well as the new technologies developed will be presented. Our evaluation showed that the system is promising, achieving high task completion rate and good user satisfation.

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Courses

  • Artificial Intelligence

    CSCI561

  • Database Systems

    CSCI585

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