Predicting Library OPAC Users Cross-device Transitions

Transcription

1 Predicting Library OPAC Users Cross-device Transitions Authors: Dan Wu, Shaobo Liang School of Information Management Wuhan University

2 utline Background Related Work Research Design Results Analysis Discussion

3 Part 1 Background

4 Part 1: Background Meet their information needs Are we still in need of the library or digital library?

5 Part 1: Background Journals Papers Videos OPAC Music Books Newspaper

6 Part 1: Background

7 Part 1: Background Different Context Digital Library Transitions between different devices

8 Part 1: Background OPAC Books Users Digital Library Search Downloads Prints Improvement & Reuse Digital Library

9 Part 1: Background Research Question What kind of activities do library OPAC users conduct before and after device transition? What are the important features to predict the next device and the activity after device transition?

10 Related Work Part 2

11 Part 2: Related Work 1. OPAC Usage in Multi-Device Environment Despite the popularity of the general search engine (e.g. Google, Bing, Baidu), the professional information resource still plays an important role in meeting the user s information need and has its advantages. Especially for the academic information, users would choose the digital library for searching papers, books, course video and others. Most users hold positive evaluation towards mobile library services and mobile phone applications. With the popularity of cross-device transition, researchers also studied the OPAC users query reformulation patterns on different devices (mobile phones, tablets and desktop), finding that transition patterns about search fields are influenced by input function and device interface

12 Part 2: Related Work 2. Prediction of Cross-Device Transition Previous work about the cross-device transition focused on the searching condition, finding that the information need about adult and shopping triggered more device transition. The prediction of cross-device is also an important aspect in this domain. The features about user s web history, touch interaction on mobile devices and the time interval of device transition can support predict the user s search behavior after device transition The machine learning has got more attention in the digital library domain. The theory and technology of machine learning can provide a valuable support for digital library to develop more intelligent digital services

13 Part 2: Related Work 2. Prediction of Cross-Device Transition Such as: Li et al. used a semi-supervised machine learning framework, combining with traditional literature retrieval methods, to construct a ranking model for document retrieval structures. Sun et al. analyzed the transaction log data of digital library users in a year to predict user s preference changes and proposed a method of predicting users future behavior preferences. Digital library also can re-rank the search results of OPAC according to the correlation between search results and user preferences, when users submit the queries in the OPAC system

14 Part 2: Related Work 2. Prediction of Cross-Device Transition Summary: The above research on cross-device search is mainly concerned with information on the general search engine. There is a lack of research on OPAC searching. Previous studies on OPAC were conducted from the aspects of user groups, search methods, and library services. There exists a lack of studies that consider both OPAC user s behavior and the impact of different devices.

15 Research Design Part 3

16 Part 3: Research Design Data Collection 16,140,509 records 3 type devices Large-scale Transaction Log February October 2016

17 Part 3: Research Design Data Collection

18 Part 3: Research Design Data Collection Browsing: it means that users browse the webpages on OPAC system, such as browsing the book information and search results. Clicking: it means that users click the particular links on OPAC. For example, he/she clicks the notice or news of digital library. Personal Account: it refers to users browse the personal information, like borrow history, in user center. Querying: when users issue the query in the search bar on OPAC, the OPAC will record their queries and search field. System Services: it means that users reset their personal password or communicated with the support staff.

19 Part 3: Research Design Unique User Identification Can t identify the IP Address unique user Not mandatory for users to login

20 Part 3: Research Design Unique User Identification 121 unique users Cross-device transition in this study means that two consequent records of the same user contain different device codes within 24 hours, and this threshold helps to cover more device transitions

21 Part 3: Research Design Data Analysis Data cleaning: We have cleaned a large number of data without user account information in order to identify unique users by programming. K-fold cross-validation: to improve the accuracy of model, which could avoid the low prediction accuracy due to the small scale of sample.. RMSE: was used to evaluate the predict performance of the different feature groups.

22 Part 4 Results Analysis

23 Part 4: Result Analysis 1. Transitions between different devices Majority cross-device transitions occurred between PCs, followed by PC-Phone and Phone-PC Users tend to use the same device to continue using OPAC.

24 Part 4: Result Analysis 2. Temporal characteristics of cross-device transition Hours when cross-device transition occurred

25 Part 4: Result Analysis 2. Temporal characteristics of cross-device transition Time interval when cross-device transition occurred

26 Part 4: Result Analysis 3. Activity in pre-transition and posttransition Activity changes after device transition

27 Part 4: Result Analysis 4. Feature importance of transition prediction Type Activity in pre-transition and post- transition Device in pre- transition and post- transition Features and Counts pre_transition_activity (x5) post_transition_activity (x5) whether_the_activity_is_same (x2) pre_transition_device (x3) post_transition_device (x3) whether_the_device_is_same (x2) A1 A2 WI D1 D2 WD

28 Part 4: Result Analysis 4. Feature importance of transition prediction Type Device s operation system in pretransition and post- transition Time Features and Counts pre_transition_operation_system (x8) post_transition_operation_system (x8) whether_the_operation_system_i s_same (x2) time_interval_of_device_transitio n time_spent_in_pretransition_session time_spent_in_postransition_session O1 O2 WO T T1 T2

29 Part 4: Result Analysis 4. Feature importance of transition prediction Target variable Post_transition_device (D2) Post_transition_activity (A2) Features Beta A1.112 Predicting next device T O D1.032 T

30 Part 4: Result Analysis 4. Feature importance of transition prediction Target variable Post_transition_device (D2) Post_transition_activity (A2) Features Beta A1.322 Predicting next activity T D2.109 O1.081 T

31 Part 4: Result Analysis 5. Prediction performance of feature group Feature Group Value of RMSE A Predicting next device A1+T A1+T+O A1+T+O1+D A1+T+O1+D1+T

32 Part 4: Result Analysis 5. Prediction performance of feature group Feature Group Value of RMSE A Predicting next activity A1+T A1+T+D A1+T+D2+O A1+T+D2+O1+D

33 Part 5 Discussion

34 Part 5: Discussion Unlike cross-device search on the general Web, device transition in OPAC search mainly occurs between PC and PC. (1) Users can easily use OPAC without being forced to login with personal accounts, which results in a smaller amount of data with unique user s information we can analyze. (2) Unlike searches on the general search engines like Google, users often use OPAC to learn and work, and this corresponds to more complex information needs.

35 Part 5: Discussion Searching activities accounted for the highest proportion after device transition, while clicking accounted for the most before device transition. (1) The most change of user s online activity is querying to querying. (2) This may reflect that after searching on the first device, users of OPAC would continue to search on the next device, being unsatisfied about the former results or being influenced by external factors.

36 Part 5: Discussion With the help of machine learning methods and tools, the intelligent information retrieval system can achieve different search results according to different unique users even if they submit the same query. Through the research on user behavior preference of digital library, it helps to provide better rank of search results and provide better preference based services. Users behavior prediction is necessary and help to recommend users much better queries, links and services after device transitions.

37 Part 5: Discussion Implication Where device transition occurred between different device, we can use the device information, such as the IP or device code, to analyze users context, e.g. their location. Library OPAC systems can provide tools or resources or services to help users resume their search tasks based on the user's activity history. Users behavior prediction is necessary and help to recommend users much better queries, links and services after device transitions. Predicting the user s behavior should consider the various origin of data, such as the data from different devices. Predicting the next device and next activity can help libraries to provide more intelligent and device-appropriate services.

38 Part 5: Discussion Limitation Lack of qualitative study. The sample size of unique users is small. Less discussion on methodologies.

39 Thank you! To Library of Wuhan University To And to you