Attribute Aware Anonymous Recommender Systems

Stritt, Manuel; Tso, Karen H. L.; Schmidt-Thieme, Lars

doi:10.1007/978-3-540-70981-7_57

Cited by 4 publications

(3 citation statements)

References 9 publications

(4 reference statements)

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“…The work of ShaweTaylor and Cristianini [14] presents a similar algorithm, too. Finally, there is another independent work on optimizing the Sales Assistant system by Stritt [16]. The systems of Stritt did not significantly perform better than the status quo system, as it was the case in our work.…”

Section: Related Workcontrasting

confidence: 44%

A learning-based hybrid approach for anonymous recommendation

Knuchel¹,

Stoianovic

2006

The 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computin

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In this paper we present a novel approach for anonymous recommendation that combines the knowledge-based recommendation with a learning component. The novelty of the approach is specially expressed through a new approach for cascade combination of two systems, that enables better the usage of prior knowledge in the learning process. Secondly, the training data for learning is based on a complex description of a recommendation situation, that enables significant improvement in the quality of recommendations in unseen situations. The approach has been implemented in a real e-commerce environment and tested in large dataset based on the twenty months usage data. The results of the evaluation studies show that this kind of hybrid systems is very promising for ephemeral recommendation.

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Section: Related Workcontrasting

confidence: 44%

A learning-based hybrid approach for anonymous recommendation

Knuchel¹,

Stoianovic

2006

The 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computin

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“…Other domains of datasets have also been looked into. For instance, music [33], digital cameras [34], other e-commerce site purchasing transactions [4], etc. However, more diverse datasets are usually corporate asset and thus are not made publicly available.…”

Section: Data For Evaluating Rs Algorithmsmentioning

confidence: 99%

Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data

Tso¹,

Schmidt-Thieme²

2006

JCP

Self Cite

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Abstract-As the amount of online shoppers grows rapidly, the need of recommender systems for e-commerce sites are demanding, especially when the number of users and products being offered online continues to increase dramatically. There have been many ongoing researches on recommender systems and in investigating recommendation algorithms that could optimize the recommendation quality. However, adequate and public datasets of users and products have always been demanding to better evaluate recommender system algorithms. Yet, the amount of public data, especially data containing adequate content information (attributes) is limited. When evaluating recommendation algorithms, it is important to observe the behavior of the algorithm as the characteristic of data varies. Synthetic data would allow the application of systematic changes on the data which cannot be done with real-life data. Although studies on synthetic data for the use of recommender systems have been investigated, artificial data with attributes information are rarely looked into. In this paper, we review public and synthetic data that are applied in the field of recommender systems. A synthetic data generation methodology that considers attributes will also be discussed. Furthermore, we present empirical evaluations on existing attributeaware recommendation algorithms and other state-of-theart algorithms using real-life dataset as well as variable synthetic data to observe their behavior as the characteristic of data varies. In particular, the informativeness of attributes is being further investigated with both real-life datasets with augmented attributes sets as well as synthetic datasets with attributes. We have shown that a reasonably good overview of the behavior of attribute-aware algorithms can be obtained by using synthetic data compared to results done with real-life datasets.

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“…They discuss the set of items which are optimal in certain information theoretic approach to learn the user, while ours uses the history as well as the user responses to learn the user of current session. Our approach can also help cold start problems ( [2], [3], [4]), in particular a new user problem, by using a nonhistory dependent distance measure, e.g., distance defined using the features of the items and we consider this for future reasearch. We propose a hybrid algorithm that combines the main ideas of UR-LA and CF policies.…”

Section: Introductionmentioning

confidence: 99%

User response based recommendations: A local angle approach

Kavitha

Memon

Hanawal

et al. 2018

2018 10th International Conference on Communication Systems &Amp; Networks (COMSNETS)

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Abstract-When a user interested in a service/item, visits an online web-portal, it provides description of its interest through initial search keywords. The system recommends items based on these keywords. The user is satisfied if it finds the item of its choice and the system benefits, otherwise the user explores an item from the list. Usually when the user explores an item, it picks an item that is nearest to its interest from the list. While the user explores an item, the system recommends new list of items. This continues till either the user finds its interest or quits. In all, the user provides ample chances and feedback for the system to learn its interest. The aim of this paper is to exploit the user-generated responses in the same session. One can further utilize the history (e.g., previous user ratings) to design good recommendation policies. We develop algorithms that efficiently utilize user responses to recommended items and find the item of user's interest quickly. We first derive optimal policies in the continuous Euclidean space and adapt the same to the space of discrete items. In the continuous Euclidean space, the optimal recommendations (e.g., with two recommendations) at the same time step are at 180 degrees from each other, while are at 90 degrees with respect to the ones at the previous time step. We propose the notion of local angle in the space of discrete items and develop user response-local angle (UR-LA) based recommendation policies. We compared the performance of UR-LA with widely used collaborative filtering (CF) based policies on two real datasets and showed that UR-LA performs better in majority of the test cases. We also proposed a hybrid scheme that combines the best features of both UR-LA and CF (and history) based policies, which outperforms them in most of the cases.

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Attribute Aware Anonymous Recommender Systems

Cited by 4 publications

References 9 publications

A learning-based hybrid approach for anonymous recommendation

A learning-based hybrid approach for anonymous recommendation

Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data

User response based recommendations: A local angle approach

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