An Adaptive Personalized Recommendation Strategy Featuring Context Sensitive Content Adaptation

Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

“…According to this context similarity, for each item rated by the user the closest items are found and a recommendation list is produced based on those closest items. Although (Chedrawy & Abidi, 2006) report that using more (3 instead of 1) perspectives result in better recommendation performance, we believe that if more and more dimensions are added, they could be useless or even harmful because they introduce noise into the context similarity values. In the second stage of the recommendation process, they use case-based reasoning mediation of past cases.…”

“…Their test results showed that their method outperforms the two conventional methods in terms of recommendation accuracy and artist variety and can reasonably recommend pieces even if they have no ratings. Chedrawy & Abidi (2006) introduce PRECiSE, a collaborative case-based recommendation system and use it for music playlist recommendation. First an item based collaborative filtering (Linden, 2003 ;Sarwar et.al., 2001) is performed, instead of a single value for item-item similarity, a vector of similarities is used.…”

“…A weighted sum of the past similar cases are stored into the case database to represent the current user. According to F1 measure, (Chedrawy & Abidi, 2006) show that they get significantly better results with the addition of the case-based reasoning stage. Li et.al.…”

“…Different feature groups we consider correspond to perspectives of (Chedrawy & Abidi, 2006) which had to be explicitly selected by the user. Finding out the appropriate feature group in this way also automatically allows for dynamic user modeling as in (Rolland, 2001).…”

Assessment of Learning in Digital Interactive Social Networks: A Learning Analytics Approach

“…At other times, data sets are collected at least in part directly for the purpose of applying learning analytics to the results. Data collection can include, for instance, an adaptive recommender where ratings on prior experiences are solicited for the purposes of prediction of respondent interest in future experiences (Chedrawy & Abidi, 2006;Dagger, Wade, & Conlan, 2005), or evidentiary data collection for educational or professional development, to address personalized or grouped components to support the learner in educational assessment (Brady, Conlan, Wade, & Dagger, 2006;Kennedy & Draney, 2006). …”