Using the wisdom of the crowds for keyword generation

Fuxman, Ariel; Tsaparas, Panayiotis; Achan, Kannan

doi:10.1145/1367497.1367506

Cited by 110 publications

(67 citation statements)

References 24 publications

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“…For example, some approaches relied on the text relevance among several text streams like query, keyword, ad copy, or the landing pages [22,23], some employed the graph information from query logs or ad click logs [24,25], other works like Hillard et al [26] imported both text relevance and graph information into the learning model as features, and recently, Hui et al added the keyword monetization ability to maximize the relevance and revenue [27].…”

Section: Relevance Modeling Optimizationmentioning

confidence: 99%

Adaptive modeling for large-scale advertisers optimization

Wang

Huang

et al. 2017

Big Data Anal

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Background: Advertisers optimization is one of the most fundamental tasks in paid search, which is a multi-billion industry as a major part of the growing online advertising market. As paid search is a three-player game (advertisers, search users and publishers), how to optimize large-scale advertisers to achieve their expected performance becomes a new challenge, for which adaptive models have been widely used. Main body: In this paper, we provide a review of the recent progress on adaptive modeling for the task of large-scale advertisers optimization in paid search, including keyword recommendation which can automatically suggest the relevant and competitive keywords to match queries of users input, bid suggestion which can efficiently give rational bids to help win the participated auctions and budget optimization which helps the advertisers show ads throughout the whole period. In addition, some related practical tools of advertiser optimization are introduced. Conclusion: Finally, we conclude that it has attracted much attention on large-scale advertisers optimization in both industry and research community and has achieved tremendous advance over the recent decade, especially for the adaptive models. Moreover, we discuss possible directions of future research on advertisers optimization.

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Section: Relevance Modeling Optimizationmentioning

confidence: 99%

Adaptive modeling for large-scale advertisers optimization

Wang

Huang

et al. 2017

Big Data Anal

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“…For example, Craswell and Szummer [16] studied the usage of Markov random walks over the bipartite graph constructed from click logs for document ranking. Fuxman et al [22] developed a random walk algorithm for keyword generation from query click graphs. This approach was also adopted in [1] to extract subtopics related to a query from the ODP taxonomy.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, there has been much work that exploits explicit subtopics using various types of Web resources. Some take suggested queries from commercial search engines as subtopics of a query [35]; others mine the subtopics from resources such as taxonomies [1,22], query logs [21,33], anchor text and Web ngrams [20].…”

Section: Introductionmentioning

confidence: 99%

Combining implicit and explicit topic representations for result diversification

Hollink

Vries

2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Result diversification deals with ambiguous or multi-faceted queries by providing documents that cover as many subtopics of a query as possible. Various approaches to subtopic modeling have been proposed. Subtopics have been extracted internally, e.g., from retrieved documents, and externally, e.g., from Web resources such as query logs. Internally modeled subtopics are often implicitly represented, e.g., as latent topics, while externally modeled subtopics are often explicitly represented, e.g., as reformulated queries.We propose a framework that: i) combines both implicitly and explicitly represented subtopics; and ii) allows flexible combination of multiple external resources in a transparent and unified manner. Specifically, we use a random walk based approach to estimate the similarities of the explicit subtopics mined from a number of heterogeneous resources: click logs, anchor text, and web n-grams. We then use these similarities to regularize the latent topics extracted from the top-ranked documents, i.e., the internal (implicit) subtopics. Empirical results show that regularization with explicit subtopics extracted from the right resource leads to improved diversification results, indicating that the proposed regularization with (explicit) external resources forms better (implicit) topic models. Click logs and anchor text are shown to be more effective resources than web n-grams under current experimental settings. Combining resources does not always lead to better results, but achieves a robust performance. This robustness is important for two reasons: it cannot be predicted which resources will be most effective for a given query, and it is not yet known how to reliably determine the optimal model parameters for building implicit topic models.

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“…For example, Craswell and Szummer [4] use a random walk on the bipartite clickgraph to adjust the ranking of the search engine. In order to generate a set of appropriate keywords for an online advertiser, Fuxman et al [9] uses an approach where a bipartite query-click graph is constructed. Given a set of URLs that are of interest to the advertiser, their method suggest keyword for the URL based upon performing a random walk with absorbing states on the bipartite click graph.…”

Section: Related Workmentioning

confidence: 99%

“Tell Me More”: Finding Related Items from User Provided Feedback

2011

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Abstract. The results returned by a search, datamining or database engine often contains an overload of potentially interesting information. A daunting and challenging problem for a user is to pick out the useful information. In this paper we propose an interactive framework to efficiently explore and (re)rank the objects retrieved by such an engine, according to feedback provided on part of the initially retrieved objects. In particular, given a set of objects, a similarity measure applicable to the objects and an initial set of objects that are of interest to the user, our algorithm computes the k most similar objects. This problem, previously coined as 'cluster on demand ' [10], is solved by transforming the data into a weighted graph. On this weighted graph we compute a relevance score between the initial set of nodes and the remaining nodes based upon random walks with restart in graphs. We apply our algorithm "Tell Me More" (TMM) on text, numerical and zero/one data. The results show that TMM for almost every experiment significantly outperforms a k-nearest neighbor approach.

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Using the wisdom of the crowds for keyword generation

Cited by 110 publications

References 24 publications

Adaptive modeling for large-scale advertisers optimization

Adaptive modeling for large-scale advertisers optimization

Combining implicit and explicit topic representations for result diversification

“Tell Me More”: Finding Related Items from User Provided Feedback

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