Personalization in Context: Does Context Matter When Building Personalized Customer Models?

Gorgoglione, Michele; Palmisano, Cosimo; Tuzhilin, Alexander

doi:10.1109/icdm.2006.125

Cited by 22 publications

(22 citation statements)

References 14 publications

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“…Also, the question of how to represent context, once it is inferred to model customers' behavior, has not been addressed before, except in [1] where it was only tangentially referred to and explored within recommender systems. This paper is built on our previous work on contextual profiling [14], [24]. In particular, we have consolidated and enhanced our prior studies of questions 1 and 2 from [14] and [24], respectively.…”

Section: Prior Workmentioning

confidence: 99%

“…This paper is built on our previous work on contextual profiling [14], [24]. In particular, we have consolidated and enhanced our prior studies of questions 1 and 2 from [14] and [24], respectively. We also added question 3, expanded our data analysis to an additional data set, and integrated studies of questions 1 and 3 into a self-contained journal paper.…”

Section: Prior Workmentioning

confidence: 99%

“…This true story is very symptomatic of problems pertaining to many personalization systems that often infer customer behavior from the registration and the purchasing information of online customers without studying the contexts in which these purchases are made. In the previous example, if the system knew that the purchase was made in the context of a gift, this transaction should have been discarded from inferring that customer's behavior, and the whole problem would have been avoided [14], [24]. Getting such contextual data, such as the intent of a purchase, is not easy in many applications.…”

Section: Introductionmentioning

confidence: 99%

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Using Context to Improve Predictive Modeling of Customers in Personalization Applications

Palmisano

Tuzhilin

Gorgoglione

2008

IEEE Trans. Knowl. Data Eng.

Self Cite

172

107

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Abstract-The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information.

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Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Context to Improve Predictive Modeling of Customers in Personalization Applications

Palmisano

Tuzhilin

Gorgoglione

2008

IEEE Trans. Knowl. Data Eng.

Self Cite

172

107

View full text Add to dashboard Cite

show abstract

“…An interest shift can be defined as abrupt change in user interests (Godoy and Amandi, 2009). Unlike interest drift which is a gradual interest change and which has been dealt with by the Forget Agent in section 3.2.1, the interest shift is more difficult to recognize as it happens suddenly (Gorgoglione et al, 2006). Users can sometimes become suddenly interested in some topics and then once these topics lose their importance, they simply abandon them.…”

Section: Short-term Layermentioning

confidence: 99%

“…Such systems have been developed for different domains of application (Pignotti et al, 2004;Challam et al, 2007;Sieg et al, 2007;Pan et al, 2007). In e-commerce, such systems have been used to recommend new items and products to users based on their previous purchasing history (Gorgoglione et al, 2006;Huang et al, 2004), while in e-learning, they are used to provide personalized e-learning services (Sun and Xie, 2009;Zhuhadar and Nasraoui, 2008). Personalization systems require and maintain information about users, and this may include demographic data, interests, preferences, and previous history.…”

Section: Introductionmentioning

confidence: 99%

Dynamic user profiles for web personalisation

Hawalah

Fasli

2015

Expert Systems with Applications

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Web personalization systems are used to enhance the user experience by providing tailor-made services based on the user's interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users' changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalization system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilizes our dynamic user profile to provide a personalized search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours.

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Mining Web search engines for query suggestion

Xu¹,

Luo²,

Yu³

et al. 2010

Concurrency and Computation

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Queries to Web search engines are usually short and ambiguous, which provides insufficient information needs of users for effectively retrieving relevant Web pages. To address this problem, query suggestion is implemented by most search engines. However, existing methods do not leverage the contradiction between accuracy and computation complexity appropriately (e.g. Google's 'Search related to' and Yahoo's 'Also Try'). In this paper, the recommended words are extracted from the search results of the query, which guarantees the real time of query suggestion properly. A scheme for ranking words based on semantic similarity presents a list of words as the query suggestion results, which ensures the accuracy of query suggestion. Moreover, the experimental results show that the proposed method significantly improves the quality of query suggestion over some popular Web search engines (e.g. Google and Yahoo). Finally, an offline experiment that compares the accuracy of snippets in capturing the number of words in a document is performed, which increases the confidence of the method proposed by the paper.We select five queries including 'sarah palin', 'iphone', 'obama', 'heath ledge', and 'jonas brothers' from Table III and search them into Google news † † † . The average percentage overlap of these five queries is 71.2%. Given the real-time query suggestions, this percentage overlap should help increase the confidence in the method proposed by the paper. CONCLUSIONS AND FUTURE WORKAverage length of the Web search query happens to be about 2.5 words; hence, it is difficult to appropriately identify query intent. This paper attempts to determine the query intent from the snippets of results served by various search engines. In this paper, a new method for query suggestion is proposed. The suggested concepts are extracted from the search result pages of † † † http://news.google.com/.

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Personalization in Context: Does Context Matter When Building Personalized Customer Models?

Abstract: Abstract

Cited by 22 publications

References 14 publications

Using Context to Improve Predictive Modeling of Customers in Personalization Applications

Using Context to Improve Predictive Modeling of Customers in Personalization Applications

Dynamic user profiles for web personalisation

Mining Web search engines for query suggestion

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