Choosing attribute weights for item dissimilarity using clikstream data with an application to a product catalog map

Kagie, Martijn; Wezel, Michiel van; Groenen, Patrick J. F.

doi:10.1145/1454008.1454040

Cited by 4 publications

(7 citation statements)

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“…In order to predict an edge between x and y, we consider the locality N = Γ(x) ∪ Γ(y) and restrict our discussion to N throughout this section. If there is an edge between two items, we assume that the edge has arrived because the attributes of 1 |.| denotes the term by term absolute value of a vector (e.g |[−2, 3]| = [2,3]) and w is the weight vector. , under the imposition of A: (∆w(1, x) and ∆w(2, x)) are not very high (Similarly for the pair (3, y) and (4, y)).…”

Section: Local Weights and Reference Dissimilarity Functionmentioning

confidence: 99%

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Local learning of item dissimilarity using content and link structure

Desarkar

Ganguly

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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In the Recommendation Problem, it is often important to find a set of items similar to a particular item or a group of items. This problem of finding similar items for the recommendation task may also be viewed as a link prediction problem in a network, where the items can be treated as the nodes. The strength of the edge connecting two items represents the similarity between the items. In this context, a central challenge is to suitably define an appropriate dissimilarity function between the items. For content based recommender systems, the dissimilarity function should take into account the individual attributes of the items. The same attribute may have different importances in different parts of the underlying network. We focus on the problem of learning a suitable dissimilarity function between items and address it by formulating it as a constrained optimization problem which captures the local weightages of the attributes in different regions of the graph. The constraints are imposed in such a way that the non-connected nodes show higher value of dissimilarity than the connected nodes. The local tuning of the weights learns the optimal value of weights in various parts of the network: from the portions having rich graph information to the portions having only content information. Detailed experimentation shows the superiority of the proposed algorithm over the Adamic Adar metric as well as logistic regression methodology.

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Section: Local Weights and Reference Dissimilarity Functionmentioning

confidence: 99%

“…Many researchers have devised methodologies to derive these weights. [3] uses a poisson regression model to find suitable attribute weights using clickstream data. Some researchers have exploited the knowledge of link information to learn the weights.…”

Section: Introductionmentioning

confidence: 99%

Local learning of item dissimilarity using content and link structure

Desarkar

Ganguly

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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“…To handle also multi-valued categorical attributes, which are categorical attributes for which a product can belong to multiple categories, this dissimilarity framework was extended in [22]. There, the dissimilarity score for multi-valued categorical attributes was defined as…”

Section: Multidimensional Scalingmentioning

confidence: 99%

“…Using the coefficients of this model and their corresponding standard errors, we compute t-values which form the basis of our attribute weights. This approach was described earlier in [22].…”

Section: Determining Attribute Weights Using Clickstream Analysismentioning

confidence: 99%

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Map Based Visualization of Product Catalogs

Kagie

Wezel

Groenen

2010

Recommender Systems Handbook

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AND KEYWORDS AbstractTraditionally, recommender systems present recommendations in lists to the user. In contentand knowledge-based recommendation systems these list are often sorted on some notion of similarity with a query, ideal product specification, or sample product. However, a lot of information is lost in this way, since two even similar products can differ from the query on a completely different set of product characteristics. When using a two dimensional, that is, a map-based, representation of the recommendations, it is possible to retain this information. In the map we can then position recommendations that are similar to each other in the same area of the map.Both in science and industry an increasing number of two dimensional graphical interfaces have been introduced over the last years. However, some of them lack a sound scientific foundation, while other approaches are not applicable in a recommendation setting. In our chapter, we will describe a framework, which has a solid scientific foundation (using state-of-the-art statistical models) and is specifically designed to work with e-commerce product catalogs. Basis of the framework is the Product Catalog Map interface based on multidimensional scaling. Also, weshow another type of interface based on nonlinear principal components analysis, which provides an easy way in constraining the space based on specific characteristic values. Then, we discuss some advanced issues. Firstly, we discuss how the product catalog interface can be adapted to better fit the users' notion of importance of attributes using click stream analysis.Secondly, we show an user interface that combines recommendation by proposing with the map based approach. Finally, we show how these methods can be applied to a real e-commerce product catalog of MP3-players.Free Keywords map-based interface, multidimensional scaling, nonlinear principal components analysis, recommender systems, dissimilarity measure Availability The ERIM Report Series is distributed through the following platforms:

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