Evaluation metrics for measuring bias in search engine results

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society

Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

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

Meta-evaluation of online and offline web search evaluation metrics

As in most information retrieval (IR) studies, evaluation plays an essential part in Web search research. Both offline and online evaluation metrics are adopted in measuring the performance of search engines. Offline metrics are usually based on relevance judgments of query-document pairs from assessors while online metrics exploit the user behavior data, such as clicks, collected from search engines to compare search algorithms. Although both types of IR evaluation metrics have achieved success, to what extent can they predict user satisfaction still remains under-investigated. To shed light on this research question, we meta-evaluate a series of existing online and offline metrics to study how well they infer actual search user satisfaction in different search scenarios. We find that both types of evaluation metrics significantly correlate with user satisfaction while they reflect satisfaction from different perspectives for different search tasks. Offline metrics better align with user satisfaction in homogeneous search (i.e. ten blue links) whereas online metrics outperform when vertical results are federated. Finally, we also propose to incorporate mouse hover information into existing online evaluation metrics, and empirically show that they better align with search user satisfaction than click-based online metrics

An Efficient Clustering System for the Measure of Page (Document) Authoritativeness

A collection of documents D1 of a search result R1 is a cluster if all the documents in D1 are similar in a way and dissimilar to another collection say D2 for a given query Q1. Implying that, given a new query Q2, the search result R2 may pose an intersection or a union of documents from D1 and D2 or more to form D3. However within these collections say D1, D2, D3 etc, one or two pages certainly would be better in relevance to the query that invokes them. Such a page is regarded being ‘authoritative’ than others. Therefore in a query context, a given search result has pages of authority. The most important measure of a search engine’s efficiency is the quality of its search results. This work seeks to cluster search results to ease the matching of searched documents with user’s need by attaching a page authority value (pav). We developed a classifier that falls in the margin of supervised and unsupervised learning which would be computationally feasible and producing most authoritative pages. A novel searching and clustering engine was developed using several measure-factors such as anchor text, proximity, page rank, and features of neighbors to rate the pages so searched. Documents or corpora of known measures from the Text Retrieval Conference (TREC), the Initiative for the Evaluation of XML Retrieval (INEX) and Reuter’s Collection, were fed into our work and evaluated comparatively with existing search engines (Google, VIVISIMO and Wikipedia). We got very impressive results based on our evaluation. Additionally, our system could add a value – pav to every searched and classified page to indicate a page’s relevance over the other. A document is a good match to a query if the document model is likely to generate the query, which will in turn happen if the document contains the query words often. This approach thus provides a different realization of some of the basic ideas for document ranking which could be applied through some acceptable rules: number of occurrence, document zone and relevance measures. The biggest problem facing users of web search engines today is the quality of the results they get back. While the results are often amusing and expand users' horizons, they are often frustrating and consume precious time. We have made available a better page ranker that do not depend heavily on the page developer’s inflicted weights but considers the actual factors within and without the target page. Though very experimental on research collections, the user can within the collection of the first ten search results listing, extract his or her relevant pages with ease. Keywords: page Authoritativeness, page Rank, search results, clustering algorithm, web crawling

Query refinement for patent prior art search

A patent is a contract between the inventor and the state, granting a limited time period to the inventor to exploit his invention. In exchange, the inventor must put a detailed description of his invention in the public domain. Patents can encourage innovation and economic growth but at the time of economic crisis patents can hamper such growth. The long duration of the application process is a big obstacle that needs to be addressed to maximize the benefit of patents on innovation and economy. This time can be significantly improved by changing the way we search the patent and non-patent literature.Despite the recent advancement of general information retrieval and the revolution of Web Search engines, there is still a huge gap between the emerging technologies from the research labs and adapted by major Internet search engines, and the systems which are in use by the patent search communities.In this thesis we investigate the problem of patent prior art search in patent retrieval with the goal of finding documents which describe the idea of a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Other relevance evidences (e.g. classification tags, and bibliographical data) provide additional details about the underlying information need of the query patent. The first goal of this thesis is to estimate a uni-gram query model from the textual fields of a query patent. We then improve the initial query representation using noun phrases extracted from the query patent. We show that expansion in a query-dependent manner is useful.The second contribution of this thesis is to address the term mismatch problem from a query formulation point of view by integrating multiple relevance evidences associated with the query patent. To do this, we enhance the initial representation of the query with the term distribution of the community of inventors related to the topic of the query patent. We then build a lexicon using classification tags and show that query expansion using this lexicon and considering proximity information (between query and expansion terms) can improve the retrieval performance. We perform an empirical evaluation of our proposed models on two patent datasets. The experimental results show that our proposed models can achieve significantly better results than the baseline and other enhanced models

Query expansion based on relevance feedback and latent semantic analysis

Web search engines are one of the most popular tools on the Internet which are widely-used by expert and novice users. Constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expansion is introduced. This method which is a combination of relevance feedback and latent semantic analysis, finds the relative terms to the topics of user original query based on relevant documents selected by the user in relevance feedback step. The method is evaluated and compared with the Rocchio relevance feedback. The results of this evaluation indicate the capability of the method to better representation of user’s information need and increasing significantly user satisfaction

Evaluating the retrieval effectiveness of Web search engines using a representative query sample

Search engine retrieval effectiveness studies are usually small-scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, data was collected using specialised software, the Relevance Assessment Tool (RAT). We found that while Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3 per cent of cases whereas Bing only found the correct answer 76.6 per cent of the time. We conclude that search engine performance on navigational queries is of great importance, as users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines