Negative Feedback Methodology

Figure 5.1: Negative Feedback for Useru1

We illustrate our user-related negative feedback methodology in Figure 5.1. Useru1 has tagged documentd1 and with tagst1,t2 and t3, and documentd2 with tag t3. As the user has used t1, t2 and t3 in the past, these are the positive tags foru1 and make up the positive User Tags (UT) recommendation set for the user. Document d1 has also been tagged by another user u2 with different tags t4 and t5. Traditional weight spreading methods would use this information to construct positive relationships between u1 and t4, and between u1 and t5, as these tags are relatively close tou1 in the graph. However, we argue thatt4andt5 should be given negative weights with regard to user u1 instead. User u1 has tagged and, in his view, adequately described documentd1 with tagst1,t2 andt3. He did not require tagst4 and t5 to describe the document and used the tags he prefers instead. Since

a different user u2 did assign those tags to d1, there is some evidence that these are possible tags for d1 and thus there was a probability that u1 could have used the same tags as well. One could argue that u1 had an opportunity to use tags t4 and t5 but consciously chose not to do so which indicates that u1 has a negative relationship with these tags. Along the same lines, we would also assign a negative weight to t6 with regard to user u1. However, the weak point of this argument is that despite the added evidence over previous approaches,u1 might still not be aware of the tags he did not use, and might want to use them in the future if they are recommended.

Figure 5.2: Negative Feedback for Documentd1

A much more convincing case can be made for negative feedback with regard to documents. In Figure 5.2, document d1 has been tagged witht1 and t2 by user u1, and with t3 by user u2. These tags are the positive tags for document d1 and make up the Document Tags (DT) recommendation set. Useru1 has also tagged a different documentd2 with the tagst4 andt5, which we classify as having a negative relationship with d1. Here the argument for negative feedback is much stronger. Since useru1 has used all of the tagst1,t2,t3 andt4 in the past, it can be assumed that u1 is aware of all of the tags’ existence. He is using the different tag sets of

{t1, t2}and{t3, t4}to distinguish between the documentsd1 andd2. Similarly, user u2 is using tag sets {t3} and {t6} to distinguish between documentsd1 and d3, so we also addt6 to the negative tag set ofd1.

While this interpretation is more plausible than previous approaches, there are some additional factors that are not considered and would be interesting to

explore in the future. The proposed approach assumes that the user is aware of all tags he has used in the past, and that he has described the each document completely, assigning all of the appropriate tags from his tag vocabulary. However, users might be forgetful of their past tagging activity, or might be focused on only certain aspects of the document at the time of tagging. So the tags identified as being negatively related might still be relevant to the document. These issues are lessened to some extent by our our method of aggregating negative feedback and our metrics (described in the next section), however, the time aspect of tagging could also be included in the interpretation of the data. An improved weighting scheme for the negative tags could then be constructed taking recency into account. If a tag is found to have a negative relationship with a document and has not been used in a long time by the user (to tag other documents), then less weight could be given to the negative score of the tag. On the other hand if a tag is identified as a negative and has been used recently by the user for different documents, then the negative score of the tag could receive more weight. The time aspect of tagging and including it in the interpretation of the data is a promising topic that would be worthwhile to explore in the future.

With both our user-related and document-related methodologies there could be cases where positive as well as implicit negative evidence is present for a user or document. For example in the document-related approach if a user has tagged a documentdwith tagt, and another user has also tagged the same documentdbut not used tagtdespite having t in their tag vocabulary. In these cases we include t only in the positive tag set ofdand not in the negative one. The reasoning behind this is that since the interpretation of positive and negative tags fordis aggregated over all users who have tagged d, it would not make sense to allow the implicit negative feedback to further influence the explicit positive feedback. The positive score of each tag included in the positive set already includes the information that only a fraction of the users who have taggeddhave assigned tag t to it. The lower the fraction of users that have assigned tagt tod out of all users that have added d to their collections, the weaker the positive score of t will be with regard to d. The sets of tags in our positive and negative groups for a user or document are thus complements without any overlap.

The set of tags included in our negative group corresponds directly to the set of tags found in the 2-hop neighbourhood of the query nodes when using our PathRank ranking algorithm (on the regular folksonomy graph) from the previous chapter. For tag nodes which are further than two hops away from the user or document we make no interpretation as to whether they are negatively related.

This part of our approach is similar to the missing values method of [Rendle et al., 2009], however, less of the tags are interpreted as negative and more tags are left as having missing evidence in our approach.