Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender

Abstract

We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumption in the context of social tagging systems. Cognitive research on how people assign tags has shown that they draw on two interconnected levels of knowledge in their memory: on a conceptual level of semantic fields or topics, and on a lexical level that turns patterns on the semantic level into words. Another strand of tagging research reveals a strong impact of time dependent forgetting on users' tag choices, such that recently used tags have a higher probability being reused than "older" tags. In this paper, we align both strands by implementing a computational theory of human memory that integrates the two-level conception and the process of forgetting in form of a tag recommender and test it in three large-scale social tagging datasets (drawn from BibSonomy, CiteULike and Flickr).As expected, our results reveal a selective effect of time: forgetting is much more pronounced on the lexical level of tags. Second, an extensive evaluation based on this observation shows that a tag recommender interconnecting both levels and integrating time dependent forgetting on the lexical level results in high accuracy predictions and outperforms other well-established algorithms, such as Collaborative Filtering, Pairwise Interaction Tensor Factorization, FolkRank and two alternative time dependent approaches. We conclude that tag recommenders can benefit from going beyond the manifest level of word co-occurrences, and from including forgetting processes on the lexical level.

Evaluating Tag Recommender Algorithms in Real-World Folksonomies

“…Additionally, the cognitive-inspired BLLAC +MPr approach that has the highest recommender accuracy also provides fair results in terms of the other metrics. Interestingly, this is not the case for the other cognitive-inspired algorithm 3LT+MPr which has poor runtime and memory consumption estimates since it requires a computationally expensive topic calculation step (see [9]). …”

The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

“…Research papers Model of human categorization [17,23,35] Activation processes in human memory [18,21,24,37] Informal learning se ings [5][6][7] Resource recommendations Research papers A ention-interpretation dynamics [15,34] Tag and time information [27,28] Recommendation evaluation Research papers Real-world folksonomies [20] Technology enhanced learning se ings [16] Hashtag recommendations…”

Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach

“…First simulation-based analyses of large-scale social tagging datasets (Kowald et al, 2014) have shown this strategy to be successful in modeling and predicting users' tag choices. The question, however, whether these results (i.e., high prediction accuracies) generalize to a realistic information search scenario, and whether tags can be recommended the employees actually adopt for their tag assignments, remains open and is addressed in the following.…”

“…An example for a very simple strategy is represented by "most popular" recommenders which assume that what has been applied by many in the past is a good predictor for future assignments. Despite their simplicity, Most Popular Tag (MPT) recommenders work surprisingly well in predicting tag reuse in offline studies (Jäschke et al, 2007;Kowald et al, 2014).…”