12,567 research outputs found
The Best Trail Algorithm for Assisted Navigation of Web Sites
We present an algorithm called the Best Trail Algorithm, which helps solve
the hypertext navigation problem by automating the construction of memex-like
trails through the corpus. The algorithm performs a probabilistic best-first
expansion of a set of navigation trees to find relevant and compact trails. We
describe the implementation of the algorithm, scoring methods for trails,
filtering algorithms and a new metric called \emph{potential gain} which
measures the potential of a page for future navigation opportunities.Comment: 11 pages, 11 figure
N-Grams Assisted Long Web Search Query Optimization
Commercial search engines do not return optimal search results when the query is a long or multi-topic one [1]. Long queries are used extensively. While the creator of the long query would most likely use natural language to describe the query, it contains extra information. This information dilutes the results of a web search, and hence decreases the performance as well as quality of the results returned. Kumaran et al. [22] showed that shorter queries extracted from longer user generated queries are more effective for ad-hoc retrieval. Hence reducing these queries by removing extra terms, the quality of the search results can be improved. There are numerous approaches used to address this shortfall. Our approach evaluates various versions of the query, thus trying to find the optimal one. This variation is achieved by reducing the query length using a combination of n-grams assisted query selection as well as a random keyword combination generator. We look at existing approaches and try to improve upon them. We propose a hybrid model that tries to address the shortfalls of an existing technique by incorporating established methods along with new ideas. We use the existing models and plug in information with the help of n-grams as well as randomization to improve the overall performance while keeping any overhead calculations in check
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
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A Genetic Algorithm Assisted Hybrid Approach to Web Information Integration
Heterogeneity and interoperability of Web data sources represent the current key issue in Web information extraction and integration. Warehouse approach and virtual approach are the common approaches adopted to integrate heterogeneous Web data sources. However, few analytic model and cost model were developed to measure and assess the efficiency and effectiveness of either approach or a combination. Hence, a contingency model cannot be produced to assist the search engine to select and mix the warehouse method and the virtual method. In this study, we present a genetic algorithm assisted hybrid approach to aid the search engine to evaluate the cost and performance factors. We apply genetic algorithm technique to formulate a cost optimization model and compute and compare the cost of extraction and integration. The cost model is based on a collection and compilation of the property data of the query analysis and path expression of the involved Web data sources. Six property analyses are conducted and six evolution steps are created to formulate the genetic algorithm of optimization. Further, we conduct a preliminary experiment using 15 local and global Web bookstores to install and test the method. Our experimental results show that the cost optimization can be achieved with the genetic algorithm and factor analysis
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