64 research outputs found

    Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

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    On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

    Combinatorial Problems in Online Advertising

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    Electronic commerce or eCommerce refers to the process of buying and selling of goods and services over the Internet. In fact, the Internet has completely transformed traditional media based advertising so much so that billions of dollars of advertising revenue is now flowing to search companies such as Microsoft, Yahoo! and Google. In addition, the new advertising landscape has opened up the advertising industry to all players, big and small. However, this transformation has led to a host of new problems faced by the search companies as they make decisions about how much to charge for advertisements, whose ads to display to users, and how to maximize their revenue. In this thesis we focus on an entire suite of problems motivated by the central question of "Which advertisement to display to which user?". Targeted advertisement happens when a user enters a relevant search query. The ads are usually displayed on the sides of the search result page. Internet advertising also takes place by displaying ads on the side of webpages with relevant content. While large advertisers (e.g. Coca Cola) pursue brand recognition by advertisement, small advertisers are happy with instant revenue as a result of a user following their ad and performing a desired action (e.g. making a purchase). Therefore, small advertisers are often happy to get any ad slot related to their ad while large advertisers prefer contracts that will guarantee that their ads will be delivered to enough number of desired users. We first focus on two problems that come up in the context of small advertisers. The first problem we consider deals with the allocation of ads to slots considering the fact that users enter search queries over a period of time, and as a result the slots become available gradually. We use a greedy method for allocation and show that the online ad allocation problem with a fixed distribution of queries over time can be modeled as maximizing a continuous non-decreasing submodular sequence function for which we can guarantee a solution with a factor of at least (1- 1/e) of the optimal. The second problem we consider is query rewriting problem in the context of keyword advertisement. This problem can be posed as a family of graph covering problems to maximize profit. We obtain constant-factor approximation algorithms for these covering problems under two sets of constraints and a realistic notion of ad benefit. We perform experiments on real data and show that our algorithms are capable of outperforming a competitive baseline algorithm in terms of the benefit due to rewrites. We next consider two problems related to premium customers, who need guaranteed delivery of a large number of ads for the purpose of brand recognition and would require signing a contract. In this context, we consider the allocation problem with the objective of maximizing either revenue or fairness. The problems considered in this thesis address just a few of the current challenges in e-Commerce and Internet Advertising. There are many interesting new problems arising in this field as the technology evolves and online-connectivity through interactive media and the internet become ubiquitous. We believe that this is one of the areas that will continue to receive greater attention by researchers in the near future

    Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

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    Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital

    Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

    Full text link
    On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

    Query Understanding in the Age of Large Language Models

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    Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language models (LLM). In this position paper, we describe a generic framework for interactive query-rewriting using LLMs. Our proposal aims to unfold new opportunities for improved and transparent intent understanding while building high-performance retrieval systems using LLMs. A key aspect of our framework is the ability of the rewriter to fully specify the machine intent by the search engine in natural language that can be further refined, controlled, and edited before the final retrieval phase. The ability to present, interact, and reason over the underlying machine intent in natural language has profound implications on transparency, ranking performance, and a departure from the traditional way in which supervised signals were collected for understanding intents. We detail the concept, backed by initial experiments, along with open questions for this interactive query understanding framework.Comment: Accepted to GENIR(SIGIR'23

    Semantic Annotation and Search: Bridging the Gap between Text, Knowledge and Language

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    In recent years, the ever-increasing quantities of entities in large knowledge bases on the Web, such as DBpedia, Freebase and YAGO, pose new challenges but at the same time open up new opportunities for intelligent information access. These knowledge bases (KBs) have become valuable resources in many research areas, such as natural language processing (NLP) and information retrieval (IR). Recently, almost every major commercial Web search engine has incorporated entities into their search process, including Google’s Knowledge Graph, Yahoo!’s Web of Objects and Microsoft’s Satori Graph/Bing Snapshots. The goal is to bridge the semantic gap between natural language text and formalized knowledge. Within the context of globalization, multilingual and cross-lingual access to information has emerged as an issue of major interest. Nowadays, more and more people from different countries are connecting to the Internet, in particular the Web, and many users can understand more than one language. While the diversity of languages on the Web has been growing, for most people there is still very little content in their native language. As a consequence of the ability to understand more than one language, users are also interested in Web content in other languages than their mother tongue. There is an impending need for technologies that can help in overcoming the language barrier for multilingual and cross-lingual information access. In this thesis, we face the overall research question of how to allow for semantic-aware and cross-lingual processing of Web documents and user queries by leveraging knowledge bases. With the goal of addressing this complex problem, we provide the following solutions: (1) semantic annotation for addressing the semantic gap between Web documents and knowledge; (2) semantic search for coping with the semantic gap between keyword queries and knowledge; (3) the exploitation of cross-lingual semantics for overcoming the language barrier between natural language expressions (i.e., keyword queries and Web documents) and knowledge for enabling cross-lingual semantic annotation and search. We evaluated these solutions and the results showed advances beyond the state-of-the-art. In addition, we implemented a framework of cross-lingual semantic annotation and search, which has been widely used for cross-lingual processing of media content in the context of our research projects
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