Search engine users regularly re-issue queries that are the same or similar to ones they have previously issued. In this paper we study this act of query re-issuing, called re-search, focusing on multi session re-searching from an information seeking perspective. By focusing on the series of repeat or similar queries where the user shows a continued interest, new patterns of behavior not previously seen arise. We find that the well-studied re-finding behavior is only a piece of the re-search puzzle, and that even amidst repeated re-findings users exhibit diversification and novelty seeking behaviours for many re-search queries. This suggests diversity and re-finding behaviors should be jointly modelled and captured in evaluation measures, instead of being studied as two separate problems as is seen in many previous approaches.

Web applications often rely on user profiles of observed user actions, such as queries issued, page views, etc. In audience selection for display advertising, the audience that is likely to be responsive to a given ad campaign is identified via such profiles. We formalize the audience selection problem as a ranked retrieval task over an index of known users. We focus on the common case of audience selection where a small seed set of users who have previously responded positively to the campaign is used to identify a broader target audience. The actions of the users in the seed set are aggregated to construct a query, the query is then executed against an index of other user profiles to retrieve the highest scoring profiles. We validate our approach on a real-world dataset, demonstrating the trade-offs of different user and query models and that our approach is particularly robust for small campaigns. The proposed user modeling framework is applicable to many other applications requiring user profiles such as content suggestion and personalization.

As the abundance of information on the Internet grows, an increasing burden is placed on the user to specify his or her query precisely in order to avoid extraneous results that may be relevant, but not useful. At the same time, users have a tendency to repeat their search behaviors, seeking the same URL (re-finding) as well as issuing the same query (re-searching). These repeated actions reveal a form of user preference that the search engine can utilize to personalize the results.

Individuals often use search engines to return to web pages they have previously visited. This behaviour, called re-finding, accounts for about 38% of all queries. While researchers have shown how re-finding differs from traditionally studied new-findings, research on how to predict and utilize re-finding is limited. In this paper we explore re-finding for personalized search. We compared three machine learning algorithms (decision trees, Bayesian multinomial regression and support vector machines) to identify re-findings. We then propose several re-ranking methods to utilize the prediction, including promoting predicted re-finding URLs and combining re-finding prediction with relevance estimation. The experimental results demonstrate that using re-finding predictions can improve retrieval performance for personalized search.

Although Web search engines are targeted towards helping people find new information, people regularly use them to re-find Web pages they have seen before. Researchers have noted the existence of this phenomenon, but relatively little is understood about how re-finding behavior differs from the finding of new information. This paper dives deeply into the differences via analysis of three large-scale data sources: 1) query logs (queries, clicks, result impressions), 2) Web browsing logs (URL visits), and 3) a daily Web crawl (page content). It appears that people learn valuable information about the pages they find that helps them re-find what they are looking for later; compared to the initial finding query, re-finding queries are typically shorter, and rank the re-found URL higher. While many instances of re-finding probably serve as a type of bookmark for a known URL, others seem to represent the resumption of a previous task; results clicked at the end of a session are more likely than those at the beginning to be re-found during a later session, while re-finding is more likely to happen at the beginning of a session than at the end. Additionally, we observe differences in cross-session and intra-session re-finding that may indicate different types of re-finding tasks. Our findings suggest there is a rich opportunity for search engines to take advantage of re-finding behavior as a means to improve the search experience.

Different buyers exhibit different purchasing behaviors. Some rush to purchase new products while others tend to be more cautious, waiting for reviews from people they trust. In market analysis, the former group of buyers is often referred to as innovators and early adopters while the latter group is referred to as laggards. The adoption behavior is a dynamic feature of the user and varies over groups of products, e.g., innovators of literature may not be the innovators of electronics. The adoption order of users is a dynamic feature of the product, which can help to predict the future potential buyers. However, such dynamic features are usually unavailable in the description of products. In this paper, we study the user behavior of an online review website- Epinions.com. We first propose to model user adoption behaviors by creating a total ordering among users who rate the products in a given category. We develop a greedy algorithm and a Markov-chain based algorithm for computing the category total ordering. Next, we show that by using user behavior information, we can more accurately predict the category of a new product as well as predict which users will follow. Furthermore, by using the Epinion.com trust network as evidence, we demonstrate that our total ordering can group users into communities that closely resemble the trust network. Thus the adoption order can be a useful feature in recommendation systems.