DYNAMIC FACET ORDERING FOR FACETED PRODUCT SEARCH ENGINES
ABSTRACT
Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list offacets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time todevise an effective list. Second, with a fixed list of facets it can happen that a facet becomes useless if all products that match thequery are associated to that particular facet. In this work, we present a framework for dynamic facet ordering in e-commerce.Based on measures for specificity and dispersion of facet values, the fully automated algorithm ranks those properties and facetson top that lead to a quick drill-down for any possible target product. In contrast to existing solutions, the framework addressese-commerce specific aspects, such as the possibility of multiple clicks, the grouping of facets by their corresponding properties,and the abundance of numeric facets. In a large-scale simulation and user study, our approach was, in general, favorably comparedto a facet list created by domain experts, a greedy approach as baseline, and a state-of-the-art entropy-based solution.
EXISTING SYSTEM:
We can find approaches in the literature that focus onpersonalized faceted search. However,we do not discuses these, as, unlike our approach, theyrequire some sort of explicit user ratings. Therefore,we only consider related work that does not requireany explicit user input other than the query.The faceted search system proposed in focuseson both textual and structured content. Given akeyword query, the proposed system aims to find theinteresting attributes, which is based on how surprisingthe aggregated value is, given the expectation. Themain contribution of this work is the navigationalexpectation, which is, according to the authors, a novelinterestingness measure achieved through judiciousapplication of p-values. This method is likely notto be suitable for the domain of e-commerce, wherealso small data sets occur and statistically derivinginteresting attributes is not possible.In, a framework for general-domain facet selectionis proposed, with the aim to maximize therank promotion of desired documents. There are manyaspects in the proposed approach that make it notapplicable in an e-commerce environment. First, twomain assumptions are made: (1) the search processis initiated using a keyword-based query, and (2) theresult is a ranked list of documents. These are seriouslimitations, as many Web shop users start with afacet selection instead of a keyword-based search, andproduct ranking is often not supported. Therefore,the framework we propose does not use these twoassumptions. Second, the proposed solution does notconsider multiple iterations of the search process (i.e.,multiple drill-downs).
PROPOSED SYSTEM:
In order to deal with this problem, we propose anapproach for dynamic facet ordering in the e-commercedomain. The focus of our approach is to handledomains with sufficient amount of complexity in termsof product attributes and values. Consumer electronics(in this work ‘mobile phones’) is one good example ofsuch a domain. As part of our solution, we devise analgorithm that ranks properties by their importanceand also sorts the values within each property. Forproperty ordering, we identify specific propertieswhose facets match many products (i.e., with a highimpurity). The proposed approach is based on a facetimpurity measure, regarding qualitative facets in asimilar way as classes, and on a measure of dispersionfor numeric facets. The property values are ordered
CONCLUSION
In this work, we proposed an approach that automaticallyorders facets such that the user finds its desiredproduct with the least amount of effort. The main ideaof our solution is to sort properties based on their facetsand then, additionally, also sort the facets themselves.We use different types of metrics to score qualitativeand numerical properties. For property ordering wewant to rank properties descending on their impurity,promoting more selective facets that will lead to a quickdrill-down of the results. Furthermore, we employ aweighting scheme based on the number of matchingproducts to adequately handle missing values and takeinto account the property product coverage.We evaluate our solution using an extensive set ofsimulation experiments, comparing it to three otherapproaches. While analyzing the user effort, especiallyin terms of the number of clicks, we can concludethat our approach gives a better performance than thebenchmark methods and in some cases even beatsthe manually curated ‘Expert-Based’ approach. Inaddition, the relatively low computational time makesit suitable for use in real-world Web shops, makingour findings also relevant to industry. These resultsare also confirmed by a user-based evaluation studythat we additionally performed.In future we would like to replicate our study on adifferent domain than cell phones, thereby addressingone of the limitations of the current evaluation. Alsowe would like to investigate the use of other metrics,such as facet and product popularity, for determiningthe order and optimal set of facets.
REFERENCES
[1]H. Zo and K. Ramamurthy, “Consumer Selection of ECommerceWebsites in a B2C Environment: A Discrete DecisionChoice Model,” IEEE Transactions on Systems, Man and Cybernetics,Part A: Systems and Humans, vol. 39, no. 4, pp. 819–839,2009.
[2] M. Hearst, “Design Recommendations for Hierarchical FacetedSearch Interfaces,” in 29th Annual International Conference onResearch & Development on Information Retrieval (ACM SIGIR2006). ACM, 2006, pp. 1–5.
[3]D. Tunkelang, “Faceted Search,” Synthesis Lectures on InformationConcepts, Retrieval, and Services, vol. 1, no. 1, pp. 1–80, 2009.
[4] K.-P. Yee, K. Swearingen, K. Li, and M. Hearst, “FacetedMetadata for Image Search and Browsing,” in Proceedings ofthe SIGCHI Conference on Human factors in Computing Systems.ACM, 2003, pp. 401–408.
[5]J. C. Fagan, “Usability Studies of Faceted Browsing: A LiteratureReview,” Information Technology and Libraries, vol. 29, no. 2,p. 58, 2010.
[6]M. Hearst, A. Elliott, J. English, R. Sinha, K. Swearingen, and K.P.Yee, “Finding the Flow in Web Site Search,” Communicationsof the ACM, vol. 45, no. 9, pp. 42–49, 2002.
[7]B. Kules, R. Capra, M. Banta, and T. Sierra, “What DoExploratory Searchers Look at in a Faceted Search Interface?”in 9th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL2009). ACM, 2009, pp. 313–322.
[8]Amazon.com, “Large US-based online retailer,” http://www.amazon.com, 2014.
[9]V. Sinha and D. R. Karger, “Magnet: Supporting Navigation inSemi-structured Data Environments,” in 24th ACM SIGMODInternational Conference on Management of Data (SIGMOD 2005).ACM, 2005, pp. 97–106.
[10]Kieskeurig.nl, “Major Dutch price comparison engine with detailedproduct descriptions,” http://www.kieskeurig.nl, 2014.
[11] Tweakers.net, “Dutch IT-community with a dedicated pricecomparison department,” http://www.tweakers.net, 2014.
[12]Q. Liu, E. Chen, H. Xiong, C. H. Ding, and J. Chen, “EnhancingCollaborative Filtering by User Interest Expansion viaPersonalized Ranking,” IEEE Transactions on Systems, Man, andCybernetics, Part B: Cybernetics, vol. 42, no. 1, pp. 218–233, 2012.
[13] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl,“An Algorithmic Framework for Performing CollaborativeFiltering,” in 22nd Annual International Conference on Researchand Development in Information Retrieval (ACM SIGIR 1999).ACM, 1999, pp. 230–237.