acl acl2010 acl2010-204 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jia Wang ; Qing Li ; Yuanzhu Peter Chen ; Zhangxi Lin
Abstract: The variety of engaging interactions among users in social medial distinguishes it from traditional Web media. Such a feature should be utilized while attempting to provide intelligent services to social media participants. In this article, we present a framework to recommend relevant information in Internet forums and blogs using user comments, one of the most representative of user behaviors in online discussion. When incorporating user comments, we consider structural, semantic, and authority information carried by them. One of the most important observation from this work is that semantic contents of user comments can play a fairly different role in a different form of social media. When designing a recommendation system for this purpose, such a difference must be considered with caution.
Reference: text
sentIndex sentText sentNum sentScore
1 cn iq Abstract The variety of engaging interactions among users in social medial distinguishes it from traditional Web media. [sent-6, score-0.334]
2 Such a feature should be utilized while attempting to provide intelligent services to social media participants. [sent-7, score-0.31]
3 In this article, we present a framework to recommend relevant information in Internet forums and blogs using user comments, one of the most representative of user behaviors in online discussion. [sent-8, score-0.568]
4 When incorporating user comments, we consider structural, semantic, and authority information carried by them. [sent-9, score-0.318]
5 One of the most important observation from this work is that semantic contents of user comments can play a fairly different role in a different form of social media. [sent-10, score-0.577]
6 When designing a recommendation system for this purpose, such a difference must be considered with caution. [sent-11, score-0.289]
7 Various engaging interactions among users in social media differ- entiate it from traditional Web sites. [sent-20, score-0.445]
8 Such characteristics should be utilized in attempt to provide intelligent services to social media users. [sent-21, score-0.31]
9 In self-publication, or customer-generated media, a user can publish an article or post news to share with others. [sent-23, score-0.373]
10 Other users can read and comment on the posting and these comments can, in turn, be read and commented on. [sent-24, score-0.989]
11 The user experience with the system can be immensely enhanced with the recommended articles. [sent-38, score-0.29]
12 In this work, we focus on recommendation in Internet fobrant creation, sharing, and collaboration among the users (Ahn et al. [sent-39, score-0.336]
13 In a discussion thread, the original posting is typically followed by other readers’ opinions, in the form of comments. [sent-48, score-0.569]
14 Apparently, there is a need to consider topic evolution in adaptive content-based recommendation and this requires novel techniques in order to capture topic evolution precisely and to prevent drastic topic shifting which returns completely irrelevant articles to users. [sent-54, score-0.825]
15 In this work, we present a framework to recommend relevant information in Internet forums and blogs using user comments, one of the most representative recordings of user behaviors in these forms of social media. [sent-55, score-0.728]
16 We model the relationship among comments and that relative to the original posting using graphs in order to evaluate their combined impact. [sent-58, score-0.747]
17 In addition, the weight of a comment is further enhanced with its content and with the authority of its poster. [sent-59, score-0.524]
18 2 Related Work In a broader context, a related problem is contentbased information recommendation (or filtering). [sent-60, score-0.289]
19 Most information recommender systems select articles based on the contents of the original post- ings. [sent-61, score-0.434]
20 The relevant news selections of these work are determined by the textual similarity between the recommended news and the original news posting. [sent-63, score-0.695]
21 , 1999) combine the news content with numerical user ratings. [sent-67, score-0.341]
22 Lee and Park (Lee and Park, 2007) consider matching between news article attributes and user preferences. [sent-72, score-0.373]
23 Some go even further by ignoring the news contents and only using browsing behaviors of the readers with similar interests (Das et al. [sent-80, score-0.369]
24 TDT consists of breaking the stream of news into individual news stories, monitoring the stories for events that have not been seen before, and categorizing them (Lavrenko and Croft, 2001). [sent-85, score-0.329]
25 A topic is modeled with a language profile deduced by the news. [sent-86, score-0.288]
26 Most existing TDT schemes calculate the similarity between a piece of news and a topic profile to determine its topic relevance (Lavrenko and Croft, 2001) (Yang et al. [sent-87, score-0.555]
27 , 2009) apply TDT techniques to group news for collaborative news recommendation. [sent-90, score-0.292]
28 Most recent researches on information recommendation in social media focus on the blogosphere. [sent-94, score-0.56]
29 That is, the knowledge in the blogosphere is enriched by such engaging interactions among bloggers and readers as posting, commenting and tagging. [sent-98, score-0.314]
30 Prior to this work, the linking structure and user tagging mechanisms in the blogosphere are the most widely adopted ones to model such collective wisdom. [sent-99, score-0.274]
31 Due to the interactions between bloggers and readers, blog recommendation should not limit its input to only blog postings themselves but also incorporate feedbacks from the readers. [sent-107, score-0.756]
32 We first describe the design of our recommendation framework in Section 3. [sent-109, score-0.289]
33 3 System Design In this section, we present a mechanism for recommendation in Internet forums and blogs. [sent-161, score-0.37]
34 Essentially, it builds a topic profile for each original posting along with the comments from readers, and uses this profile to retrieve relevant articles. [sent-163, score-1.257]
35 Then, with such collective wisdom, we use a graph to model the relationship among comments and that relative to the original posting in order to evaluate the impact of each comment. [sent-165, score-0.834]
36 This information along with the original posting and its comments are fed into a synthesizer. [sent-167, score-0.747]
37 The synthesizer balances views from both authors and readers to construct a topic profile to retrieve relevant articles. [sent-168, score-0.477]
38 1 Incorporating Comments In a discussion thread, comments made at different levels reflect the variation of focus of readers. [sent-170, score-0.272]
39 Therefore, recommended articles should reflect their concerns to complement the author’s opinion. [sent-171, score-0.323]
40 1 Authority Scoring Comments Intuitively, each comment may have a different degree of authority determined by the status of its author (Hu et al. [sent-178, score-0.425]
41 We consider the cases that a user replies to a previous posting and that a user quotes a previous posting separately. [sent-184, score-1.22]
42 For user , we use to denote the number of times that has replied to user . [sent-185, score-0.28]
43 We combine them linearly: Further, we normalize the above quantity to record how frequently a user refers to another: ∑푛(푖,푘 ) + 휖 Inline with the Pa∑geRank algorithm, we define the authority of user 3. [sent-187, score-0.458]
44 2 as Differentiating comments with Semantic and Structural relations Next, we construct a similar model in terms of the comments themselves. [sent-189, score-0.45]
45 In this model, we treat the original posting and the comments each as a text node. [sent-190, score-0.747]
46 First, a comment can be made in response to the original posting or at most one earlier comment. [sent-194, score-0.769]
47 In particular, the original posting is the root and all the comments are ordinary nodes. [sent-196, score-0.747]
48 There is an arc (directed edge) from node to node , denoted , if the corresponding comment is made in response to comment (or original posting) . [sent-197, score-0.575]
49 M Figure 2: Multi-relation graph of comments based on the structural and semantic information denoted . [sent-205, score-0.326]
50 There is an arc from node to node , denoted ,if the corresponding comment quotes comment (or original posting) . [sent-206, score-0.575]
51 In some social networking media, a user may have a subset of other users as “friends”. [sent-212, score-0.378]
52 Thus, wf {it 0h, t1h}i s, winhfoosremation and assuming poster has made a comment k for user posting, the final weight of this comment is defined as ’s 2 3. [sent-214, score-0.678]
53 2 Topic Profile Construction Once the weight of comments on one posting is quantified by our models, this information along with the entire discussion thread is fed into a synthesizer to construct a topic profile. [sent-215, score-0.992]
54 It i1s a 훼li )n×e ar com(푡)bi +na 훼tio× n of the contribution by the posting itself, , and that by the comments, . [sent-220, score-0.47]
55 Thus, when the original posting and comments are each considered as a document, this term frequency can be calculated for any term in any document. [sent-225, score-0.813]
56 That is, the contribution of comment score is incorporated into weight calculation of the words in a comment. [sent-227, score-0.291]
57 m ax푤 (푡) m ax푠(푖) Such a treatment of compounded weight is essentially to recognize that readers’ impact on selecting relevant articles and the difference of their influence. [sent-228, score-0.272]
58 260 With the topic profile thus constructed, the retriever returns an ordered list of articles with decreasing relevance to the topic. [sent-230, score-0.496]
59 Note that our approach to differentiate the importance of each comment can be easily incorporated into any generic retrieval model. [sent-231, score-0.318]
60 Given original posting and recommended article , if , for a given generalization threshold ,then B is marked as a generalization. [sent-252, score-0.805]
61 4 Experimental Evaluation To evaluate the effectiveness of our proposed recommendation mechanism, we carry out a series of experiments on two synthetic data sets, collected from Internet forums and blogs, respectively. [sent-257, score-0.37]
62 This data set is constructed by randomly selecting 20 news articles with corresponding reader comments from the Digg Web site and 16,718 news articles from the Reuters news Web site. [sent-259, score-1.045]
63 This simulates the scenario of recommending relevant news from traditional media to social media users for their further reading. [sent-260, score-0.663]
64 The second one is the Blog data set containing 15 blog articles with user comments and 15,1 10 articles obtained from the Myhome Web site 2. [sent-261, score-0.873]
65 6412 453 60 The recommendation engine may return a set of essentially the same articles re-posted at different sites. [sent-270, score-0.462]
66 In our experiments, we define precision and novelty metrics as ∣ 퐶∩ 푅 ∣and where is the subset of the top- articles returned by the recommender, is the set of manually tagged relevant articles, and is the set of manually tagged relevant articles excluding duplicate ones to the original posting. [sent-272, score-0.603]
67 We select the top 10 articles for evaluation assuming most readers only browse up to 10 recommended articles (Karypis, 2001). [sent-273, score-0.595]
68 Next, we study the effect of user authority and its integration to comment weighting. [sent-278, score-0.565]
69 1 Overall Performance As baseline proposals, we also implement two well-known content-based recommendation methods (Bogers and Bosch, 2007). [sent-283, score-0.289]
70 Following the strategy of Bogers and Bosch, relevant articles are selected based on the title and the first 10 sentences of the original postings. [sent-294, score-0.28]
71 Trimming the rest of an article would usually remove relatively less crucial information, which speeds up the recommendation process. [sent-296, score-0.376]
72 Our explanation is that blog articles may not be organized in the inverted pyramid style as strictly as news forum articles. [sent-304, score-0.641]
73 1) the number of the most weighted words to represent the topic, and 2) combination coefficient to determine the contribution of original posting and comments in selecting relevant arti- cles. [sent-307, score-0.802]
74 When is set to 0, the recommended articles only reflect the author’s opinion. [sent-311, score-0.323]
75 When , the suggested articles represent the concerns of readers exclusively. [sent-312, score-0.272]
76 3 Effect of Authority and Comments In this part, we explore the contribution of user authority and comments in social media recom- mender. [sent-323, score-0.814]
77 RUN 1 (Posting): the topic profile is constructed only based on the original posting itself. [sent-328, score-0.81]
78 RUN 2 (Posting+Authority): the topic profile is constructed based on the original posting and participant authority. [sent-330, score-0.81]
79 RUN 3 (Posting+Comment): the topic profile is constructed based on the original posting and its comments. [sent-331, score-0.81]
80 RUN 4 (All): the topic profile is constructed based on the original posting, user authority, and its comments. [sent-332, score-0.48]
81 There is a step- wise performance improvement while integrating user authority, comments and both. [sent-335, score-0.365]
82 With the assistance of user authority and comments, the recommendation precision is improved up to 9. [sent-336, score-0.607]
83 Figure 3: Effect of content, quotation and reply relation Content Relation (CR): only the content relation matrix is used in scoring the comments. [sent-344, score-0.401]
84 Quotation+Reply Relation (QRR): both the quotation and reply relation matrices are used in scoring the comments. [sent-349, score-0.301]
85 For the case of Forum, we observe that incorporating content information adversely affects recommendation precision. [sent-352, score-0.344]
86 Specifically, comments in news forums usually carry much richer structural information than blogs where comments are usually “flat” among themselves. [sent-359, score-0.826]
87 4 Recommendation Interpretation To evaluate the precision of interpreting the relationship between recommended articles and the 263 original posting, the evaluation metric of success rate is defined as where is the number of recommended articles, is the error weight of recommended article . [sent-361, score-0.806]
88 Note that these rates include the errors introduced by the irrelevant articles returned by the retrieval module. [sent-366, score-0.277]
89 Traditional recommendation is essentially a push service to provide information according to the profile of individual or groups of users. [sent-376, score-0.456]
90 In this work, we present a framework for information recommendation in such social media as Internet forums and blogs. [sent-379, score-0.641]
91 This model incorporates information of user status and comment semantics and structures within the entire discussion thread. [sent-380, score-0.434]
92 By combining such information with traditional statistical language models, it is capable of suggesting relevant articles that meet the dynamic nature of a discussion in social media. [sent-382, score-0.468]
93 One important discovery from this work is that, when integrating comment contents, the structural information among comments, and reader relationship, it is crucial to distinguish the characteristics ofvarious forms of social media. [sent-383, score-0.484]
94 The reason is that the role that the semantic content of a comment plays can differ from one form to another. [sent-384, score-0.302]
95 For example, we can also evaluate its effectiveness and costs during the operation of a discussion forum, where the discussion thread is continually updated by new comments and votes. [sent-386, score-0.369]
96 Open user profiles for adaptive news systems: help or harm? [sent-397, score-0.336]
97 An intelligent news recommender agent for filtering and categorizing large volumes of text corpus. [sent-427, score-0.342]
98 An analysis of bloggers, topics and tags for a blog recommender system. [sent-449, score-0.319]
99 A synthetical approach for blog recommendation: Combining trust, social relation, and semantic analysis. [sent-486, score-0.322]
100 News recommender system based on topic detection and tracking. [sent-494, score-0.278]
wordName wordTfidf (topN-words)
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