One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search e ciency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional Query-By-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely di erent paradigm, how does one leverage previous product logs to learn ranking models and or evaluate the new system with no existing usage logs? Finally, given the di erent nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query- By-Example. This paper describes our approach to solving these challenges. We present experimental results con rming the e ectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members.
Query-By-Example ( QBE ) in image retrieval (also referred as content-based image retrieval), especially in dermatology studies, originates from the seminal work in @cite_13 @cite_27 in 1980s and 1990s. QBE in image retrieval systems typically utilizes the example images containing objects of interest to generate visual features which include color and texture for detecting and identifying the particular objects easily. More recently, there has been a focus on Query-By-Example in text domain @cite_17 @cite_9 . Text based QBE systems extract key phrases from unstructured query documents by using techniques like tf-idf and also combine them with semantic matching, such as, LSI and LDA.
@cite_9 @cite_27 @cite_13 @cite_17
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Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high-level concepts and low-level features, and (2) the subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high-level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70000 images show that the proposed approach greatly reduces the user's effort of composing a query, and captures the user's information need more precisely. We present an approach for picture indexing and abstraction. Picture indexing facilitates information retrieval from a pictorial database consisting of picture objects and picture relations. To construct picture indexes, abstraction operations to perform picture object clustering and classification are formulated. To substantiate the abstraction operations, we also formalize syntactic abstraction rules and semantic abstraction rules. We then illustrate by examples how to apply these abstraction operations to obtain various picture indexes, and how to construct icons to facilitate accessing of pictorial data. We are experiencing an unprecedented increase of content contributed by users in forums such as blogs, social networking sites and microblogging services. Such abundance of content complements content on web sites and traditional media forums such as news papers, news and financial streams, and so on. Given such plethora of information there is a pressing need to cross reference information across textual services. For example, commonly we read a news item and we wonder if there are any blogs reporting related content or vice versa. In this paper, we present techniques to automate the process of cross referencing online information content. We introduce methodologies to extract phrases from a given "query document" to be used as queries to search interfaces with the goal to retrieve content related to the query document. In particular, we consider two techniques to extract and score key phrases. We also consider techniques to complement extracted phrases with information present in external sources such as Wikipedia and introduce an algorithm called RelevanceRank for this purpose. We discuss both these techniques in detail and provide an experimental study utilizing a large number of human judges from Amazons's Mechanical Turk service. Detailed experiments demonstrate the effectiveness and efficiency of the proposed techniques for the task of automating retrieval of documents related to a query document.