Li-wei He

Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce

The Web abounds with dyadic data that keeps increasing by every single second. Previous work has repeatedly shown the usefulness of extracting the interaction structure inside dyadic data [21, 9, 8]. A commonly used tool in extracting the underlying structure is the matrix factorization, whose fame was further boosted in the Netflix challenge [26]. When we were trying to replicate the same success on real-world Web dyadic data, we were seriously challenged by the scalability of available tools. We therefore in this paper report our efforts on scaling up the nonnegative matrix factorization (NMF) technique. We show that by carefully partitioning the data and arranging the computations to maximize data locality and parallelism, factorizing a tens of millions by hundreds of millions matrix with billions of nonzero cells can be accomplished within tens of hours. This result effectively assures practitioners of the scalability of NMF on Web-scale dyadic data.

Optimal rare query suggestion with implicit user feedback

Query suggestion has been an effective approach to help users narrow down to the information they need. However, most of existing studies focused on only popular/head queries. Since rare queries possess much less information (e.g., clicks) than popular queries in the query logs, it is much more difficult to efficiently suggest relevant queries to a rare query. In this paper, we propose an optimal rare query suggestion framework by leveraging implicit feedbacks from users in the query logs. Our model resembles the principle of pseudo-relevance feedback which assumes that top-returned results by search engines are relevant. However, we argue that the clicked URLs and skipped URLs contain different levels of information and thus should be treated differently. Hence, our framework optimally combines both the click and skip information from users and uses a random walk model to optimize the query correlation. Our model specifically optimizes two parameters: (1) the restarting (jumping) rate of random walk, and (2) the combination ratio of click and skip information. Unlike the Rocchio algorithm, our learning process does not involve the content of the URLs but simply leverages the click and skip counts in the query-URL bipartite graphs. Consequently, our model is capable of scaling up to the need of commercial search engines. Experimental results on one-month query logs from a large commercial search engine with over 40 million rare queries demonstrate the superiority of our framework, with statistical significance, over the traditional random walk models and pseudo-relevance feedback models.

Query suggestion by constructing term-transition graphs

Query suggestion is an interactive approach for search engines to better understand users information need. In this paper, we propose a novel query suggestion framework which leverages user re-query feedbacks from search engine logs. Specifically, we mined user query reformulation activities where the user only modifies part of the query by (1) adding terms after the query, (2) deleting terms within the query, or (3) modifying terms to new terms. We build a term-transition graph based on the mined data. Two models are proposed which address topic-level and term-level query suggestions, respectively. In the first topic-based unsupervised Pagerank model, we perform random walk on each of the topic-based term-transition graph and calculate the Pagerank for each term within a topic. Given a new query, we suggest relevant queries based on its topic distribution and term-transition probability within each topic. Our second model resembles the supervised learning-to-rank (LTR) framework, in which term modifications are treated as documents so that each query reformulation is treated as a training instance. A rich set of features are constructed for each (query, document) pair from Pagerank, Wikipedia, N-gram, ODP and so on. This supervised model is capable of suggesting new queries on a term level which addresses the limitation of previous methods. Experiments are conducted on a large data set from a commercial search engine. By comparing the with state-of-the-art query suggestion methods [4, 2], our proposals exhibit significant performance increase for all categories of queries.

Post-ranking query suggestion by diversifying search results

Query suggestion refers to the process of suggesting related queries to search engine users. Most existing researches have focused on improving the relevance of suggested queries. In this paper, we introduce the concept of diversifying the content of the search results from suggested queries while keeping the suggestion relevant. Our framework first retrieves a set of query candidates from search engine logs using random walk and other techniques. We then re-rank the suggested queries by ranking them in the order which maximizes the diversification function that measures the difference between the original search results and the results from suggested queries. The diversification function we proposed includes features like ODP category, URL and domain similarity and so on. One important outcome from our research which contradicts with most existing researches is that, with the increase of suggestion relevance, the similarity between the queries actually decreases. Experiments are conducted on a large set of human-labeled data, which is randomly sampled from a commercial search engines log. Results indicate that the post-ranking framework significantly improves the relevance of suggested queries by comparing to existing models.

Task Trail: An Effective Segmentation of User Search Behavior

In this paper, we introduce “task trail” to understand user search behaviors. We define a task to be an atomic user information need, whereas a task trail represents all user activities within that particular task, such as query reformulations, URL clicks. Previously, web search logs have been studied mainly at session or query level where users may submit several queries within one task and handle several tasks within one session. Although previous studies have addressed the problem of task identification, little is known about the advantage of using task over session or query for search applications. In this paper, we conduct extensive analyses and comparisons to evaluate the effectiveness of task trails in several search applications: determining user satisfaction, predicting user search interests, and suggesting related queries. Experiments on large scale data sets of a commercial search engine show that: (1) Task trail performs better than session and query trails in determining user satisfaction; (2) Task trail increases webpage utilities of end users comparing to session and query trails; (3) Task trails are comparable to query trails but more sensitive than session trails in measuring different ranking functions; (4) Query terms from the same task are more topically consistent to each other than query terms from different tasks; (5) Query suggestion based on task trail is a good complement of query suggestions based on session trail and click-through bipartite. The findings in this paper verify the need of extracting task trails from web search logs and enhance applications in search and recommendation systems.

Searchable web sites recommendation

In this paper, we propose a new framework for searchable web sites recommendation. Given a query, our system will recommend a list of searchable web sites ranked by relevance, which can be used to complement the web page results and ads from a search engine. We model the conditional probability of a searchable web site being relevant to a given query in term of three main components: the language model of the query, the language model of the content within the web site, and the reputation of the web site searching capability (static rank). The language models for queries and searchable sites are built using information mined from client-side browsing logs. The static rank for each searchable site leverages features extracted from these client-side logs such as number of queries that are submitted to this site, and features extracted from general search engines such as the number of web pages that indexed for this site, number of clicks per query, and the dwell-time that a user spends on the search result page and on the clicked result web pages. We also learn a weight for each kind of feature to optimize the ranking performance. In our experiment, we discover 10.5 thousand searchable sites and use 5 million unique queries, extracted from one week of log data to build and demonstrate the effectiveness of our searchable web site recommendation system.