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CitySearcher: A City Search Engine For Interests

Published: 07 August 2017 Publication History

Abstract

We introduce CitySearcher, a vertical search engine that searches for cities when queried for an interest. Generally in search engines, utilization of semantics between words is favorable for performance improvement. Even though ambiguous query words have multiple semantic meanings, search engines can return diversified results to satisfy different users' information needs. But for CitySearcher, mismatched semantic relationships can lead to extremely unsatisfactory results. For example, the city Sale would incorrectly rank high for the interest shopping because of semantic interpretations of the words. Thus in our system, the main challenge is to eliminate the mismatched semantic relationships resulting from the side effect of the semantic models. In the previous case, we aim to ignore the semantics of a city's name which is not indicative of the city's characteristics. In CitySearcher, we use word2vec, a very popular word embedding technique to estimate the semantics of the words and create the initial ranks of the cities. To reduce the effect of the mismatched semantic relationships, we generate a set of features for learning based on a novel clustering-based method. With the generated features, we then utilize learning to rank algorithms to rerank the cities for return. We use the English version of Wikivoyage dataset for evaluation of our system, where we sample a very small dataset for training. Experimental results demonstrate the performance gain of our system over various standard retrieval techniques.

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    cover image ACM Conferences
    SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
    August 2017
    1476 pages
    ISBN:9781450350228
    DOI:10.1145/3077136
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 07 August 2017

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    Author Tags

    1. feature engineering
    2. information retrieval
    3. word2vec

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    • The Academy of Finland
    • The Natural Science Foundation of China

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    SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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