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Proceedings ArticleDOI

Time alone is not the clear indicator of relevancy

TL;DR: Whether the time spent by a user on a webpage actually indicates its relevancy to the user is determined, to address another issue of time.
Abstract: While browsing the web, the meaning and importance of time changes as perspectives change. From the communications perspective, speed to data transmission matters. The network perspective webpage load times matter. Search Engine Optimisation perspective focuses on dwell time, total time spent on each webpage from the corpus of webpage's visited. From an average user's perspective, the initial few seconds of a webpage being visited is critical to determine whether the user will stay on the webpage or leave the webpage. The probability of the user leaving the webpage is very high during these first few seconds. After the user stays on the webpage, determining whether it is relevant to the user, is he actually engaged on the content represented on the webpage during the period of the visit is important too. Higher engagement indicates the user has a higher chance of returning to the webpage. This paper tries to address another issue of time, and intends to determine, whether the time spent by a user on a webpage actually indicates its relevancy to the user.
References
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Proceedings ArticleDOI
01 Jan 2001
TL;DR: It was found that the time spent on a pages, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.
Abstract: Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can ``intelligently'' determine the interest of a user and use this information to make suggestions. The common solution, ``explicit ratings'', where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more ``intelligent'' method is to useimplicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating.Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages.Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.

768 citations

Proceedings ArticleDOI
23 May 2006
TL;DR: This paper investigates how detailed tracking of user interaction can be monitored using standard web technologies to enable implicit interaction and to ease usability evaluation of web applications outside the lab.
Abstract: In this paper, we investigate how detailed tracking of user interaction can be monitored using standard web technologies. Our motivation is to enable implicit interaction and to ease usability evaluation of web applications outside the lab. To obtain meaningful statements on how users interact with a web application, the collected information needs to be more detailed and fine-grained than that provided by classical log files. We focus on tasks such as classifying the user with regard to computer usage proficiency or making a detailed assessment of how long it took users to fill in fields of a form. Additionally, it is important in the context of our work that usage tracking should not alter the user's experience and that it should work with existing server and browser setups. We present an implementation for detailed tracking of user actions on web pages. An HTTP proxy modifies HTML pages by adding JavaScript code before delivering them to the client. This JavaScript tracking code collects data about mouse movements, keyboard input and more. We demonstrate the usefulness of our approach in a case study.

440 citations

Journal ArticleDOI
TL;DR: The importance and usefulness of tag and time information when predicting users' preference and how to exploit such information to build an effective resource-recommendation model are investigated and a recommender system is designed to realize the computational approach.
Abstract: Recently, social tagging has become increasingly prevalent on the Internet, which provides an effective way for users to organize, manage, share and search for various kinds of resources. These tagging systems offer lots of useful information, such as tag, an expression of user's preference towards a certain resource; time, a denotation of user's interests drift. As information explosion, it is necessary to recommend resources that a user might like. Since collaborative filtering (CF) is aimed to provide personalized services, how to integrate tag and time information in CF to provide better personalized recommendations for social tagging systems becomes a challenging task. In this paper, we investigate the importance and usefulness of tag and time information when predicting users' preference and examine how to exploit such information to build an effective resource-recommendation model. We design a recommender system to realize our computational approach. Also, we show empirically using data from a real-world dataset that tag and time information can well express users' taste and we also show that better performances can be achieved if such information is integrated into CF.

152 citations

Proceedings ArticleDOI
Bo Yang1, Tao Mei2, Xian-Sheng Hua2, Linjun Yang2, Shiqiang Yang1, Mingjing Li2 
09 Jul 2007
TL;DR: This paper presents a novel online video recommendation system based on multimodal fusion and relevance feedback, and is able to recommend videos without users' profiles.
Abstract: With Internet delivery of video content surging to an un-precedented level, video recommendation has become a very popular online service. The capability of recommending relevant videos to targeted users can alleviate users' efforts on finding the most relevant content according to their current viewings or preferences. This paper presents a novel online video recommendation system based on multimodal fusion and relevance feedback. Given an online video document, which usually consists of video content and related information (such as query, title, tags, and surroundings), video recommendation is formulated as finding a list of the most relevant videos in terms of multimodal relevance. We express the multimodal relevance between two video documents as the combination of textual, visual, and aural relevance. Furthermore, since different video documents have different weights of the relevance for three modalities, we adopt relevance feedback to automatically adjust intra-weights within each modality and inter-weights among different modalities by users' click-though data, as well as attention fusion function to fuse multimodal relevance together. Unlike traditional recommenders in which a sufficient collection of users' profiles is assumed available, this proposed system is able to recommend videos without users' profiles. We conducted an extensive experiment on 20 videos searched by top 10 representative queries from more than 13k online videos, reported the effectiveness of our video recommendation system.

143 citations

Proceedings ArticleDOI
11 Aug 2002
TL;DR: An evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search shows that the techniques are effective and efficient for information seeking.
Abstract: In this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search. Two specific techniques are examined: the presentation of sentences that highly match the searcher's query and the use of implicit evidence. Implicit evidence is evidence captured from the searcher's interaction with the retrieval results and is used to automatically update the display. Our evaluation concentrates on the effectiveness and subject perception of these techniques. The results show, with statistical significance, that the techniques are effective and efficient for information seeking.

139 citations