Detection of shifts in user interests for personalized information filtering

Lam, Wai; Mukhopadhyay, Snehasis; Mostafa, Javed; Palakal, Mathew

doi:10.1145/243199.243279

Cited by 48 publications

(34 citation statements)

References 5 publications

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“…Filtering systems developers typically rely upon the technique of user simulation in order to understand and quantify the effectiveness of their solutions (e.g., [2,15]). We simulate the behavior of a typical user by assuming that the user is interested in a subset of our Yahoo!…”

Section: User Simulationmentioning

confidence: 99%

Self-adaptive user profiles for large-scale data delivery

Çetintemel

Franklin

Giles³

Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)

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Section: User Simulationmentioning

confidence: 99%

Self-adaptive user profiles for large-scale data delivery

Çetintemel

Franklin

Giles³

Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)

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“…Lam et al propose a two-level approach to identify shifts in user interests [18]: a low-level machine learning algorithm for specific interests, and a higher level Bayesian analyzer for significant shifts in a general "interest profile." Kim and Chan build a user interest hierarchy, a continuum of general to specific interests based on words and phrases in web pages bookmarked by a user [19].…”

Section: Identifying User Interestsmentioning

confidence: 99%

Interest Driven Navigation in Visualization

Healey

Dennis

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These "elements of interest (EOI)" are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to "tour" areas of interest (AOI) within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a data set as interesting or not interesting to the viewer. The model responds in real time, updating the elements of interest based on a viewer's actions. This allows us to track a viewer's interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines. We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world.

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“…In (Lam, Mukhopadhyay, Mostafa and Palakal, 1996), a two-level probabilistic learning approach is used: one level for shift detection, one level for user model learning. Shifts are detected per category of interest.…”

Section: Related Workmentioning

confidence: 99%

Revisiting the effect of history on learning performance: the problem of the demanding lord

Giannakopoulos

Palpanas

2012

Knowl Inf Syst

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Abstract. In a variety of settings ranging from recommendation systems to information filtering, approaches which take into account feedback have been introduced to improve services and user experience. However, as also indicated in the machine learning literature, there exist several settings where the requirements and target concept of a learning system changes over time, which consists a case of "concept drift". In several systems a sliding window over the training instances has been used to follow drifting concepts. However, no general analytic study has been performed on the relation between the size of the sliding window and the average performance of a learning system, since previous works have focused on instantaneous performance and specific underlying learners and data characteristics.This work proposes an analytic model that describes the effect of memory window size on the prediction performance of a learning system that is based on iterative feedback. The analysis considers target concepts changing over time, either periodically or randomly, using a formulation termed "the problem of the demanding lord". Using a signal-to-noise approach to sketch learning ability of underlying machine learning algorithms, we estimate the average performance of a learning system regardless of its underlying algorithm, and, as a corollary, propose a stepping stone towards finding the memory window that maximizes the average performance for a given drift setting and a specific modeling system. We experimentally support the proposed methodology with very promising results on three synthetic and four real datasets, using a variety of learning algorithms including Support Vector Machines, Naive Bayes, Nearest Neighbor and Decision Trees on classification and regression tasks. The results validate the analysis and indicate very good estimation performance in different settings.

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Detection of shifts in user interests for personalized information filtering

Cited by 48 publications

References 5 publications

Self-adaptive user profiles for large-scale data delivery

Self-adaptive user profiles for large-scale data delivery

Interest Driven Navigation in Visualization

Revisiting the effect of history on learning performance: the problem of the demanding lord

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