Identifying interests of web users for effective recommendations

Search log data is multi dimensional data consisting of number of searches of multiple users with many searched parameters. This data can be used to identify a user’s interest in an item or object being searched. Identifying highest interests of a Web user from his search log data is a complex process. Based on a user’s previous searches, most recommendation methods employ two-dimensional models to find relevant items. Such items are then recommended to a user. Two-dimensional data models, when used to mine knowledge from such multi dimensional data may not be able to give good mappings of user and his searches. The major problem with such models is that they are unable to find the latent relationships that exist between different searched dimensions. In this research work, we utilize tensors to model the various searches made by a user. Such high dimensional data model is then used to extract the relationship between various dimensions, and find the prominent searched components. To achieve this, we have used popular tensor decomposition methods like PARAFAC, Tucker and HOSVD. All experiments and evaluation is done on real datasets, which clearly show the effectiveness of tensor models in finding prominent searched components in comparison to other widely used two-dimensional data models. Such top rated searched components are then given as recommendation to users.

Clustering of web users using the tensor decomposed models

We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.

Web 2.0 applications in enterprises and education

Web 2.0 technology and concepts are being used increasingly by organisations to enhance knowledge, efficiency, engagement and reputation. Understanding the concepts of Web 2.0, its characteristics, and how the technology and concepts can be adopted, is essential to successfully reap the potential benefits. In fact, there is a debate about using the Web 2.0 idiom to refer to the concept behind it; however, this term is widely used in literature as well as in industry. In this paper, the definition of Web 2.0 technology, its characteristics and the attributes, will be presented. In addition, the adoption of such technology is further explored through the presentation of two separate case examples of Web 2.0 being used: to enhance an enterprise; and to enhance university teaching. The similarities between these implementations are identified and discussed, including how the findings point to generic principles of adoption.

Negotiation 2.0 in the twitter age : using Web 2.0 applications to facilitate successful negotiation outcomes

The high-pressure, cross-cultural, cross-factional and frequently cross-national nature of contemporary negotiation means that there are a number of clements potentially hampering efforts to achieve successful negotiation outcomes from face-to-face interactions. These hindrances include: resource scarcity (for example, finances, technology and facilities), time scarcity, geographical separation, lack of a COnl1110n language and an inability to Inaintain a consistent ongoing dialogue.

How long is a tweet? Mapping dynamic conversation networks on Twitter using Gawk and Gephi

Twitter is now well established as the world’s second most important social media platform, after Facebook. Its 140-character updates are designed for brief messaging, and its network structures are kept relatively flat and simple: messages from users are either public and visible to all (even to unregistered visitors using the Twitter website), or private and visible only to approved ‘followers’ of the sender; there are no more complex definitions of degrees of connection (family, friends, friends of friends) as they are available in other social networks. Over time, Twitter users have developed simple, but effective mechanisms for working around these limitations: ‘#hashtags’, which enable the manual or automatic collation of all tweets containing the same #hashtag, as well allowing users to subscribe to content feeds that contain only those tweets which feature specific #hashtags; and ‘@replies’, which allow senders to direct public messages even to users whom they do not already follow. This paper documents a methodology for extracting public Twitter activity data around specific #hashtags, and for processing these data in order to analyse and visualize the @reply networks existing between participating users – both overall, as a static network, and over time, to highlight the dynamic structure of @reply conversations. Such visualizations enable us to highlight the shifting roles played by individual participants, as well as the response of the overall #hashtag community to new stimuli – such as the entry of new participants or the availability of new information. Over longer timeframes, it is also possible to identify different phases in the overall discussion, or the formation of distinct clusters of preferentially interacting participants.

Automated gathering of Web information: An in-depth examination of agents interacting with search engines

The Web has become a worldwide repository of information which individuals, companies, and organizations utilize to solve or address various information problems. Many of these Web users utilize automated agents to gather this information for them. Some assume that this approach represents a more sophisticated method of searching. However, there is little research investigating how Web agents search for online information. In this research, we first provide a classification for information agent using stages of information gathering, gathering approaches, and agent architecture. We then examine an implementation of one of the resulting classifications in detail, investigating how agents search for information on Web search engines, including the session, query, term, duration and frequency of interactions. For this temporal study, we analyzed three data sets of queries and page views from agents interacting with the Excite and AltaVista search engines from 1997 to 2002, examining approximately 900,000 queries submitted by over 3,000 agents. Findings include: (1) agent sessions are extremely interactive, with sometimes hundreds of interactions per second (2) agent queries are comparable to human searchers, with little use of query operators, (3) Web agents are searching for a relatively limited variety of information, wherein only 18% of the terms used are unique, and (4) the duration of agent-Web search engine interaction typically spans several hours. We discuss the implications for Web information agents and search engines.

Classifying the user intent of web queries using k-means clustering

Purpose: Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries. Design/methodology/approach: For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k-means clustering approach based on a variety of query traits. Findings: The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational. Research limitations/implications: This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs. Practical implications: The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research. Originality/value: This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay-off for web search engines can be quite beneficial. © Emerald Group Publishing Limited.

Searching for salvation: An analysis of US religious searching on the World Wide Web

The goals of this research were to answer three questions. How predominant is religious searching online? How do people interact with Web search engines when searching for religious information? How effective are these interactions in locating relevant information? Specifically, referring to a US demographic, we analyzed five data sets from Web search engine, collected between 1997 and 2005, of over a million queries each in order to investigate religious searching on the Web. Results point to four key findings. First, there is no evidence of a decrease in religious Web-searching behaviors. Religious interest is a persistent topic of Web searching. Second, those seeking religious information on the Web are becoming slightly more interactive in their searching. Third, there is no evidence for a move away from mainstream religions toward non-mainstream religions since the majority of the search terms are associated with established religions. Fourth, our work does not support the hypothesis that traditional religious affiliation is associated with lower adoption of or sophistication with technology. These factors point to the Web as a potentially usefully communication medium for a variety of religious organizations. © 2009 Elsevier Ltd. All rights reserved.

A temporal comparison of AltaVista Web searching

Major Web search engines, such as AltaVista, are essential tools in the quest to locate online information. This article reports research that used transaction log analysis to examine the characteristics and changes in AltaVista Web searching that occurred from 1998 to 2002. The research questions we examined are (1) What are the changes in AltaVista Web searching from 1998 to 2002? (2) What are the current characteristics of AltaVista searching, including the duration and frequency of search sessions? (3) What changes in the information needs of AltaVista users occurred between 1998 and 2002? The results of our research show (1) a move toward more interactivity with increases in session and query length, (2) with 70% of session durations at 5 minutes or less, the frequency of interaction is increasing, but it is happening very quickly, and (3) a broadening range of Web searchers' information needs, with the most frequent terms accounting for less than 1% of total term usage. We discuss the implications of these findings for the development of Web search engines. © 2005 Wiley Periodicals, Inc.

Web searcher interaction with the Dogpile.com metasearch engine

Metasearch engines are an intuitive method for improving the performance of Web search by increasing coverage, returning large numbers of results with a focus on relevance, and presenting alternative views of information needs. However, the use of metasearch engines in an operational environment is not well understood. In this study, we investigate the usage of Dogpile.com, a major Web metasearch engine, with the aim of discovering how Web searchers interact with metasearch engines. We report results examining 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005 and compare these results with findings from other Web searching studies. We collect data on geographical location of searchers, use of system feedback, content selection, sessions, queries, and term usage. Findings show that Dogpile.com searchers are mainly from the USA (84% of searchers), use about 3 terms per query (mean = 2.85), implement system feedback moderately (8.4% of users), and generally (56% of users) spend less than one minute interacting with the Web search engine. Overall, metasearchers seem to have higher degrees of interaction than searchers on non-metasearch engines, but their sessions are for a shorter period of time. These aspects of metasearching may be what define the differences from other forms of Web searching. We discuss the implications of our findings in relation to metasearch for Web searchers, search engines, and content providers.