Exploring market competition over topics in spatio-temporal document collections

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and

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“…Topic exploration [169,170]. Zhao et al [169] propose to explore topics over geo-textual objects within a specified region and time interval.…”

Section: Region Finding/analysismentioning

confidence: 99%

“…The topic assignment of a geotextual object is based on the latent dirichlet allocation (LDA) model, a commonly used topic model. In follow-on work [170], the Topic RT query is extended to capture the market competition of different brands over each topic for a category of business (e.g., coffeehouses).…”

Section: Region Finding/analysismentioning

confidence: 99%

Location- and keyword-based querying of geo-textual data: a survey

2021

Self Cite

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“…Zhao et al [46] proposed an offline pre-training method to find topics of interest to users from spatio-temporal documents such as Twitter tweets and Yelp reviews. The method first uses an offline pre-training method to produce a training model for the collected Twitter and Yelp's spatio-temporal documents.…”

Section: B Topic Acquisitionmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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“…Our previous paper is designed for the MMR‐based rank method without considering to filter the crawled information. It is proverbial that there are a number of irrelevant information or news return by Baidu or Google search engine . This makes the ranking method cannot select the relevance information or news to obtain better ranking result.…”

Section: Introductionmentioning

confidence: 99%

“…It is proverbial that there are a number of irrelevant information or news return by Baidu or Google search engine. 12 This makes the ranking method cannot select the relevance information or news to obtain better ranking result. Additionally, the cosine similarity between two documents uses the bag-of-word method, which misses the semantic link among the words within a window size.…”

mentioning

confidence: 99%

Verifying the claimed sale‐ranking trustworthy: A maximum marginal relevance‐based ranking method

Wang

Fang

Shen

et al. 2019

Concurrency and Computation

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Summary Various online contents on Internet platforms or search engines are related to the corporate reputation. Facing the huge amount of online contents, we need a mining method that can automatically extract and analyze a large number of network‐related information and obtain the real reliability of aspect for the content claimed by companies. In this paper, we propose to generate a ranking model to verify whether the sales‐rankings claimed by companies are trustworthy. The key idea is that the company that has higher confidence score should be supported by the online media. We use a unique data set of public opinion data related with a specific company, which we supplement with data from various online news platform and retrieval webpages using a distributed and generic Web crawler. Meanwhile, basic information and open financial data of companies are also collected for auxiliary analysis. We present a Maximal Marginal Relevance‐based ranking model to compute the confidence score of each company, taking into consideration the two technologies of word embedding and KL‐Divergence to filter the irrelevant documents. Extensive experiments show that the proposed method outperforms the state‐of‐the‐art MMR‐based method, and we showcase three representative cases about the corporate reputation built by us that gives positive, neutral, and negative support respectively to the sales‐ranking claim of companies.

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Exploring market competition over topics in spatio-temporal document collections

Cited by 4 publications

References 31 publications

Location- and keyword-based querying of geo-textual data: a survey

Location- and keyword-based querying of geo-textual data: a survey

A Study of Optimizing Search Engine Results Through User Interaction

Verifying the claimed sale‐ranking trustworthy: A maximum marginal relevance‐based ranking method

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