96,408 research outputs found
Constructing an Interaction Behavior Model for Web Image Search
User interaction behavior is a valuable source of implicit relevance
feedback. In Web image search a different type of search result presentation is
used than in general Web search, which leads to different interaction
mechanisms and user behavior. For example, image search results are
self-contained, so that users do not need to click the results to view the
landing page as in general Web search, which generates sparse click data. Also,
two-dimensional result placement instead of a linear result list makes browsing
behaviors more complex. Thus, it is hard to apply standard user behavior models
(e.g., click models) developed for general Web search to Web image search.
In this paper, we conduct a comprehensive image search user behavior analysis
using data from a lab-based user study as well as data from a commercial search
log. We then propose a novel interaction behavior model, called grid-based user
browsing model (GUBM), whose design is motivated by observations from our data
analysis. GUBM can both capture users' interaction behavior, including cursor
hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1)
It is based on an unsupervised learning method and does not need manually
annotated data for training. (2) It is based on user interaction features on
search engine result pages (SERPs) and is easily transferable to other
scenarios that have a grid-based interface such as video search engines. We
conduct extensive experiments to test the performance of our model using a
large-scale commercial image search log. Experimental results show that in
terms of behavior prediction (perplexity), and topical relevance and image
quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms
state-of-the-art baseline models as well as the original ranking. We make the
implementation of GUBM and related datasets publicly available for future
studies.Comment: 10 page
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
A Formal Model of Metaphor in Frame Semantics
A formal model of metaphor is introduced. It models metaphor, first, as an interaction of “frames” according to the frame semantics, and then, as a wave function in Hilbert space. The practical way for a probability distribution and a corresponding wave function to be assigned to a given
metaphor in a given language is considered. A series of formal definitions is deduced from this for: “representation”, “reality”, “language”, “ontology”, etc. All are based on Hilbert space. A few statements about a quantum computer are implied: The sodefined reality is inherent and internal to it. It can report a result only “metaphorically”. It will demolish transmitting the result “literally”, i.e. absolutely exactly. A new and different formal
definition of metaphor is introduced as a few entangled wave functions corresponding to different “signs” in different language formally defined as above. The change of frames as the change from the one to the other formal definition of metaphor is interpreted as a formal definition of thought. Four areas of cognition are unified as different but isomorphic interpretations of the mathematical model based on Hilbert space. These are: quantum mechanics, frame semantics, formal semantics by
means of quantum computer, and the theory of metaphor in
linguistics
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
Optimization is commonly employed to determine the content of web pages, such
as to maximize conversions on landing pages or click-through rates on search
engine result pages. Often the layout of these pages can be decoupled into
several separate decisions. For example, the composition of a landing page may
involve deciding which image to show, which wording to use, what color
background to display, etc. Such optimization is a combinatorial problem over
an exponentially large decision space. Randomized experiments do not scale well
to this setting, and therefore, in practice, one is typically limited to
optimizing a single aspect of a web page at a time. This represents a missed
opportunity in both the speed of experimentation and the exploitation of
possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We
formulate an approach where the possible interactions between different
components of the page are modeled explicitly. We apply bandit methodology to
explore the layout space efficiently and use hill-climbing to select optimal
content in realtime. Our algorithm also extends to contextualization and
personalization of layout selection. Simulation results show the suitability of
our approach to large decision spaces with strong interactions between content.
We further apply our algorithm to optimize a message that promotes adoption of
an Amazon service. After only a single week of online optimization, we saw a
21% conversion increase compared to the median layout. Our technique is
currently being deployed to optimize content across several locations at
Amazon.com.Comment: KDD'17 Audience Appreciation Awar
eWOM & Referrals in Social Network Services
If a few decades ago the development of the Internet was instrumental in the interconnection between markets, nowadays the services provided by Web 2.0, such as social network sites (SNS) are the cutting edge. A proof of this trend is the exponential growth of social network users. The main objective of this work is to explore the mechanisms that promote the transmission and reception (WOM and referrals) of online opinions, in the context of the
SNS, by buyers of travel services. The research includes some research lines: technology acceptance model (TAM), Social Identification Theory and Word-of-Mouth communication in virtual environment (eWOM). Based on these theories an explicative model has been proposed applying SEM analysis to a sample of SNS users’ of tourist service buyers. The results support the majority of the hypotheses and some relevant practical and theoretical
implications have been pointed out for tourist managers
- …