Personalised news and scientific literature aggregation

Nanas, Nikolaos; Vavalis, Manolis; Houstis, Elias N.

doi:10.1016/j.ipm.2009.07.005

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…Such a hybrid profile performs a combination of content-based and collaborative filtering which alleviates many of the known problems of each individual approach. We have already developed a Web prototype 14 based on Nootropia (Nanas et al 2010) and we are currently working on improving and extending this real world application of AIF to provide personalised information delivery and communication on the Web.…”

Section: Discussionmentioning

confidence: 99%

Words, antibodies and their interactions

2010

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Jerne's idiotypic network theory stresses the importance of antibody-to-antibody interactions and provides possible explanations for self-tolerance and increased diversity in the immune repertoire. In this paper, we use an immune network model to build a user profile for adaptive information filtering. Antibody-to-antibody interactions in the profile's network model correlations between words in text. The user profile has to be able to represent a user's multiple interests and adapt to changes in them over time. This is a complex and dynamic engineering problem with clear analogies to the immune process of self-assertion. We present a series of experiments investigating the effect of term correlations on the user's profile performance. The results show that term correlations can encode additional information, which has a positive effect on the profile's ability to assess the relevance of documents to the user's interests and to adapt to changes in them.

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Section: Discussionmentioning

confidence: 99%

Words, antibodies and their interactions

2010

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“…Madnick and Siegel [22] predicted increasing usage of aggregation applications owing to faster bundling of content and a minimized search costs. Nanas, et al [23] show that aggregation applications using RSs can learn from users' interests and can identify relevant content for a user. Also, Paliouras, et al [24] developed a system that automatically assigns content to various categories and presented it in a personalized interface adapted to a user's usage behavior.…”

Section: Automated News Aggregationmentioning

confidence: 99%

The Digitization of News Aggregation: Experimental Evidence on Intention to Use and Willingness to Pay for Personalized News Aggregators

Oechslein

Haim

Graefe

et al. 2015

2015 48th Hawaii International Conference on System Sciences

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Historically, journalists have manually selected news. This process has been changing dramatically with the development of personalized news aggregators (PNAs), which rely on social recommender systems (SRSs) technology. PNAs provide content geared to the personal preferences of news consumers, and thus offer new business opportunities for news providers. However, little research exists on users' intention to usePNAs or their willingness to pay (WTP) for such services. We developed PNA prototypes based on hybrid and social recommender systems and tested their performance in an online experiment. While the results showed little difference in users' intention to use either system, content provided by SRSs was perceived as more accurate. Furthermore, the optimal price point for the social recommender system (€1.68) was 68% higher than the price point for the hybrid recommender system.

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“…These systems filter information according to user preferences that can be either explicitly stated by the user or can be implicitly inferred from past behavior. Content‐based filtering is usually based on the automatic extraction of descriptive features and therefore typically constrained to information media (Nanas, Vavalis, & Houstis, ). Collaborative filtering calculates expected user preferences for an item, using evaluation by or the preferences of a set of people who have experienced the item, ignoring the content of the item (Billsus & Pazzani, ; Goldberg, Nichols, Oki, & Terry, ; Konstan et al., ; Montaner et al., ). The basic input data consist of the preference matrix between users and products.…”

Section: Personalized Recommendation Systemsmentioning

confidence: 99%

Personalizing news content: An experimental study

Sela

Lavie

Inbar

et al. 2014

Asso for Info Science & Tech

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The delivery of personalized news content depends on the ability to predict user interests. We evaluated different methods for acquiring user profiles based on declared and actual interest in various news topics and items. In an experiment, 36 students rated their interest in six news topics and in specific news items and read on 6 days standard, nonpersonalized editions and personalized (basic or adaptive) news editions. We measured subjective satisfaction with the editions and expressed preferences, along with objective measures, to infer actual interest in items. Users' declared interest in news topics did not strongly predict their actual interest in specific news items. Satisfaction with all news editions was high, but participants preferred the personalized editions. User interest was weakly correlated with reading duration, article length, and reading order. Different measures predicted interest in different news topics. Explicit measures predicted interest in relatively clearly defined topics such as sports, but were less appropriate for broader topics such as science and technology. Our results indicate that explicit and implicit methods should be combined to generate user profiles. We suggest that a personalized newspaper should contain both general information and personalized items, selected based on specific combinations of measures for each of the different news topics. Based on the findings, we present a general model to decide on the personalization of news content to generate personalized editions for readers.

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Personalised news and scientific literature aggregation

Cited by 12 publications

References 16 publications

Words, antibodies and their interactions

Words, antibodies and their interactions

The Digitization of News Aggregation: Experimental Evidence on Intention to Use and Willingness to Pay for Personalized News Aggregators

Personalizing news content: An experimental study

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