342 research outputs found
Effects of foraging in personalized content-based image recommendation
A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users' attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. We investigate a personalized content-based image recommendation system to understand what affects user attention by reinforcing visual attention cues based on IFT. We further find that visual bookmarks (cues) lead to a stronger scent of the recommended image collection. Our evaluation is based on the Pinterest image collection
The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation
User-generated online content serves as a source of product- and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review’s “tone”. However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence)
Situation Normality and the Shape of Search: The Effects of Time Delays and Information Presentation on Search Behavior
Delays have become one of the most often cited complaints of web users. Long delays often cause users to abandon their searches, but how do tolerable delays affect information search behavior? Intuitively, we would expect that tolerable delays should induce decreased information search. We conducted two experiments and found that as delay increased, a point occurs at which time within-page information search increases; that is, search behavior remained the same until a tipping point occurs where delay increases the depth of search. We argue that situation normality explains this phenomenon; users have become accustomed to tolerable delays up to a point (our research suggests between 7 and 11 s), after which search behavior changes. That is, some delay is expected, but as delay becomes noticeable but not long enough to cause the abandonment of search, an increase occurs in the “stickiness” of webpages such that users examine more information on each page before moving to new pages. The net impact of tolerable delays was counterintuitive: tolerable delays had no impact on the total amount of data searched in the first experiment, but induced users to examine more data points in the second experiment
Variability of User Interaction with Multi-Platform News Feeds
The development of the World Wide Web (WWW) and proliferation of web enabled devices have allowed various news agencies to enrich their traditional method of distribution of news through TV, radio and print with simultaneous broadcast through the Web. The varying nature of devices through which the Web is accessed warrants different ways to feed the same content. This precipitates some variation in the way users interact with the news feeds. In this paper, we investigate how mental models and information scent affect this variation and user interaction on the whole. We present results from a preliminary survey conducted to capture the current news gathering behavior of general population and verify our assumptions. We then present observations from the study conducted using BBC news site over laptop, PDA and a cell phone
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A framework for understanding user interaction with content-based image retrieval: model, interface and users
User interaction is essential to the communication between users and content-based image retrieval (CBIR) systems. User interaction covers three key elements: an interaction model, an interactive interface and users. The three key elements combine to enable effective interaction to happen. Many studies have investigated different aspects of user interaction. However, there is lack of research in combining all three elements in an integrated manner, especially through well-principled data analysis based on a systematic user study. In this thesis, we investigate the combination of all three elements for interactive CBIR.
We first propose uInteract - a framework including a novel four-factor user interaction model (FFUIM) and an interactive interface. The FFUIM aims to improve interaction and search accuracy of the relevance feedback mechanism for CBIR. The interface delivers the FFUIM visually, aiming to support users in grasping how the interaction model functions and how best to manipulate it. The framework is tested in three task-based and user-oriented comparative evaluations, which involves 12 comparative systems, 12 real life scenario tasks and 50 subjects. The quantitative data analysis shows encouraging observations on ease of use and usefulness of the proposed framework, and also reveals a large variance of the results depending on different user types.
Accordingly, based on Information Foraging Theory, we further propose a user classification model along three user interaction dimensions: information goals (I), search strategies (S) and evaluation thresholds (E) of users. To our best knowledge, this is the first principled user classification model in CBIR. The model is operated and verified by a systematic qualitative data analysis based on multi linear regression on the real user interaction data from comparative user evaluations. From final quantitative and qualitative data analysis based on the ISE model, we have established what different types of users like about the framework and their preferences for interactive CBIR systems. Our findings offer useful guidelines for interactive search system design, evaluation and analysis
Improving Reachability and Navigability in Recommender Systems
In this paper, we investigate recommender systems from a network perspective
and investigate recommendation networks, where nodes are items (e.g., movies)
and edges are constructed from top-N recommendations (e.g., related movies). In
particular, we focus on evaluating the reachability and navigability of
recommendation networks and investigate the following questions: (i) How well
do recommendation networks support navigation and exploratory search? (ii) What
is the influence of parameters, in particular different recommendation
algorithms and the number of recommendations shown, on reachability and
navigability? and (iii) How can reachability and navigability be improved in
these networks? We tackle these questions by first evaluating the reachability
of recommendation networks by investigating their structural properties.
Second, we evaluate navigability by simulating three different models of
information seeking scenarios. We find that with standard algorithms,
recommender systems are not well suited to navigation and exploration and
propose methods to modify recommendations to improve this. Our work extends
from one-click-based evaluations of recommender systems towards multi-click
analysis (i.e., sequences of dependent clicks) and presents a general,
comprehensive approach to evaluating navigability of arbitrary recommendation
networks
Building economic models and measures of search
Economics provides an intuitive and natural way to formally represent the costs and benefits of interacting with applications, interfaces and devices. By using economic models it is possible to reason about interaction, make predictions about how changes to the system will affect behavior, and measure the performance of people's interactions with the system. In this tutorial, we first provide an overview of relevant economic theories, before showing how they can be applied to formulate different ranking principles to provide the optimal ranking to users. This is followed by a session showing how economics can be used to model how people interact with search systems, and how to use these models to generate hypotheses about user behavior. The third session focuses on how economics has been used to underpin the measurement of information retrieval systems and applications using the C/W/L framework (which reports the expected utility, expected total utility, expected total cost, and so on) - and how different models of user interaction lead to different metrics. We then show how information foraging theory can be used to measure the performance of an information retrieval system - connecting the theory of how people search with how we measure it. The final session of the day will be spent building economic models and measures of search. Here sample problems will be provided to challenge participants, or participants can bring their own
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