User Study Result

We recruited 8 subjects for our user study, and they rated 211 web pages totally. We apply half-life utility measure to evaluate the performances of three different types of profiles. The rating r in half-life utility measure can be from 1 to 5 according to subjects’ ratings, and the maximum achievable utilityHU_i^max is gained by setting the

5.5. USER STUDY 71

Figure 5.10: Evaluation Result of User Study ratings of useri’s all items to 5.

We useHU@n to view the average performances among all subjects’ top-n item only, and we show the results includingHU@1, HU@3, HU@5, and HU@10. From the results in Fig. 5.10, the most similar item measured by the baseline method showed the best performance, which means the subjects gave the ratings averagely higher than the top-1 items measured by semantic based profiles. The utilities of semantic tag-based profiles tag-based on ConceptNet are the best amongHU@3, HU@5, and HU@10.

The utilities of semantic tag-based profiles based on WordNet are a little lower than the utilities of the baseline method in HU@3 and HU@5, but it becomes better in HU@10.

Chapter 6

Conclusion

In this thesis we proposed semantic based user profiles enriching the original tag-based user profiles by tag concepts. Each tag concept represents a common concept by the core tag and the set of semantic similar tags. We also proposed the similrity mea-sure for semantic tag-based user profiles which eliminates the deficiency of measuring similarity between tag-based user profiles. By applying cosine similarity in measuring the similarity between two distinct tags, we only get zero. But by applying the same method between two tag concepts, we can get the similarity if the two concepts are overlapped. By the similarity measure, we also can find out similar users or identify items a user has interests in.

Based on a user’s resource collection and associated sets of tags on social me-dia sites, we could construct the semantic tag-based user profile containing the set of tag concepts to represent the user’s interests. We introduced three semantic resources, WordNet and ConceptNet and Google snippets, with the associated approaches to

mea-73

sure semantic similarities between tags. We represented how to construct a tag concept from a tag by spreading activation with semantic similarities, and then we constructed a semantic tag-based user profile by a set of tag concepts from a user’s resource col-lection with associated tags.

From empirical evaluation, we showed the performances of the semantic tag-based user profiles based on WordNet, ConceptNet, and Google snippets all were better than the performance of the tag-based user profile with the data set consisting 20,578 users and 80,000 bookmarks by 5-fold cross validation. From the result of user study, se-mantic tag-based user profiles based on ConceptNet show the best utility excluding considering top 1 only.

6.1 Summary of Contributions

• We proposed a semantic similarity measure for tag-based profiles with appropri-ate properties, and this measure eliminappropri-ates the deficiency of measuring similarity by cosine similarity.

• We provided an insight into how the semantic tag-based profile of a user can be constructed from tags associated with the user’s social media collection, and the semantic relations preserved in the profile could reflect the user’s interests as the concepts.

• We proposed tag concepts capturing semantic relations between tags, and se-mantic similarities between tags could be measured based on different sese-mantic

6.2. FUTURE WORK 75

resources to represent different meanings. In this thesis we utilized WordNet, ConceptNet, and Google snippets to measure semantic similarity.

6.2 Future Work

According to our definition of semantic tag-based profiles, we can construct different profiles based on different approaches and semantic resources. However, it is pos-sible to combine different semantic resources with associated similarity measures to construct one semantic tag-based profile revealing better performance. Based on the same tag with different semantic resources, we may construct tag concepts including distinct set of tags and associated weights. Thus, combining all tag concepts into one is an important issue to do in the future.

The problem about how to filter dissimilar tags in a tag concept is also a research issue. Further, if we can confirm dissimilar tags when measuring the similarity between tag concepts, we can obtain more accurate semantic similarity between tag concepts and between semantic tag-based profiles probably.

In this thesis, we construct semantic tag-base profiles based on tag-based profiles which tag weights are measured by a simple approach. However, tag weights can be determined by different approaches for different circumstances. For example, we can consider temporal factor and add more tag weights on the set of tags used recently.

And we can combine those factors with our proposed solutions for different purposes.

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