Social search power-law distribution: y 3707r3172, R2 1.0005A 0.8862 (the data set included 879 users for a range of 1 to 49 tags reused) Does citeUlike Support"Social"Bookmarking? Although CiteULike supports tag reuse, many users didnt reuse tags from others'collections, although they reused tags from their own. We can explain his disparity at a human-computer interaction level. Clearly, the interface that CiteULike gives users during tagging affects their tagging behav- 0"1 357 91113 15 171921 25 27 3335373941434547 ior When users tag papers, the interface lets them (a) Reuse occurrences conveniently select and reuse tags from their per- sonal collections; when they want to reuse tags from outside their collections, however, they can't view them during tagging As mentioned previously, the only way users can deliberately reuse tags from others' collec- tion is to remember them from when they first viewed the article link; through mere coinci dence, they might also reuse a tag. Thus, CiteU- Like doesn't explicitly support reuse through social transactions, which would explain why such tag reuse is low If social bookmarking services want to encour- age greater tag reuse, they should pay particular attention to interface design. For example, in Cite Seer, we' re now designing an integrated tagging 10 13 16 19 22 25 28 31 34 37 40 4346 49 interface such that users can see existing tags Tags reused from both their personal collections and others Encouraging tag reuse requires not only Figure 5. Tag reuse. (a) For tag reuse occurrences, A"indicates that integrated tagging interface but also an appropri 1,014 tags were reused once; " B"indicates that 5 4 tags were ate tagging recommendation system. Not all exist reused twice. b) Users and the frequency of reuse occurrences from ing tags are relevant to every paper; when the their personal collections. "A"indicates that 167 users reused one number of existing tags gets sufficiently large, tag,"B"indicates that 36 users reused two tags users will be cognitively overloaded with respect to browsing and selecting relevant tags Tag rec- ommendation can address this problem by sug- We also wanted to understand how many tags gesting appropriate tags for papers based on Isers were reusing from their personal collections several criteria. Currently, CiteULike presents the (that is, how much a user reuses tags he or she has most frequently used tags (using visual enhance- applied before). The average number of tag reuse ment- that is, a larger font size) in a user's per occurrences for each user was 8.5: the median and sonal collection to that user when he or she tags a modal numbers were 5 and 1, respectively. This paper. Although tag frequency is one heuristic for dicates that users were moderately reusing tags recommending tags, it doesn't have any bearing from their personal collections when tagging new on those tags' relevance to the paper papers. Figure 5b shows the results. Data point"A A more practical way to recommend tags is to indicates that 167 users reused one tag from their compare similarities between papers and their personal collections; data point"B" indicates that associated tags. When a user is about to tag a new 136 users reused two tags from their personal col- paper, an automatic tagging recommendation sys- lections, and so on. Again, the data resembled a tem can suggest relevant tags based on similarity www.computer.org/internet/ IEEE INTERNET COMPUTINGWe also wanted to understand how many tags users were reusing from their personal collections (that is, how much a user reuses tags he or she has applied before). The average number of tag reuse occurrences for each user was 8.5; the median and modal numbers were 5 and 1, respectively. This indicates that users were moderately reusing tags from their personal collections when tagging new papers. Figure 5b shows the results. Data point “A” indicates that 167 users reused one tag from their personal collections; data point “B” indicates that 136 users reused two tags from their personal col￾lections, and so on. Again, the data resembled a power-law distribution: y = 370.7x–1.3172, R2 = 0.8862 (the data set included 879 users for a range of 1 to 49 tags reused). Does CiteULike Support “Social” Bookmarking? Although CiteULike supports tag reuse, many users didn’t reuse tags from others’ collections, although they reused tags from their own. We can explain this disparity at a human–computer interaction level. Clearly, the interface that CiteULike gives users during tagging affects their tagging behav￾ior. When users tag papers, the interface lets them conveniently select and reuse tags from their per￾sonal collections; when they want to reuse tags from outside their collections, however, they can’t view them during tagging. As mentioned previously, the only way users can deliberately reuse tags from others' collec￾tion is to remember them from when they first viewed the article link; through mere coinci￾dence, they might also reuse a tag. Thus, CiteU￾Like doesn’t explicitly support reuse through social transactions, which would explain why such tag reuse is low. If social bookmarking services want to encour￾age greater tag reuse, they should pay particular attention to interface design. For example, in Cite￾Seer, we’re now designing an integrated tagging interface such that users can see existing tags, from both their personal collections and others’. Encouraging tag reuse requires not only an integrated tagging interface but also an appropri￾ate tagging recommendation system. Not all exist￾ing tags are relevant to every paper; when the number of existing tags gets sufficiently large, users will be cognitively overloaded with respect to browsing and selecting relevant tags. Tag rec￾ommendation can address this problem by sug￾gesting appropriate tags for papers based on several criteria. Currently, CiteULike presents the most frequently used tags (using visual enhance￾ment — that is, a larger font size) in a user’s per￾sonal collection to that user when he or she tags a paper. Although tag frequency is one heuristic for recommending tags, it doesn’t have any bearing on those tags’ relevance to the paper. A more practical way to recommend tags is to compare similarities between papers and their associated tags. When a user is about to tag a new paper, an automatic tagging recommendation sys￾tem can suggest relevant tags based on similarity 20 www.computer.org/internet/ IEEE INTERNET COMPUTING Social Search Figure 5.Tag reuse. (a) For tag reuse occurrences,“A” indicates that 1,014 tags were reused once;“B” indicates that 514 tags were reused twice. (b) Users and the frequency of reuse occurrences from their personal collections.“A” indicates that 167 users reused one tag;“B” indicates that 136 users reused two tags. 0 200 400 600 800 1,000 1,200 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 A B 0 20 40 60 80 100 120 140 160 180 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 A B Tags Users Reuse occurrences Tags reused (a) (b)