DEIM Forum 2023 4b-8-3

Recommendation of Revisiting Web Pages Related to Currently
Browsing Web Pages
Wenjia ZHANG† , Yoshiyuki SHOJI†† , and Hiroaki OHSHIMA†† Graduate School of Information Science, University of Hyogo
8–2–1 Gakuen-nishimachi, Nishi-ku, Kobe, Hyogo 651–2197, Japan†† College of Science and Engineering, Aoyama Gakuin University
5–10–1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa 252–5258, Japan
E-mail: †ad22v040@guh.u-hyogo.ac.jp, ††shoji@it.aoyama.ac.jp, †††ohshima@ai.u-hyogo.ac.jp
Abstract A considerable amount of activities on the web involves revisiting web pages when processing repetitive
tasks on computers. Browser support for revisiting mainly consists of bookmarks and browsing history, or focuses on
recently visited pages. We propose a method for recommending previously visited pages that are informative for thetask in progress. Revisiting pages may be diﬀerent in the tracing process, but are eventually opened after startinga certain web activity in a temporal sense. The process of the proposed method consists of three parts. First,
we collected spaced co-occurring web pages in the browsing history. Second, we constructed a system to output
revisiting pages based on currently browsing web pages. Finally, we used this method to recommend revisited pages
in several speciﬁc tasks.
Key words recommendation system, web browsing history, information seeking behaviour

1.

Introduction

Computers are often used for processing a variety of tasks.
Some pages are used repeatedly for the same task occurring every time. Each time these repetitive tasks are done,
it is time consuming to ﬁnd pages that users would like tovisit again, and it can also be diﬃcult or even impossible toﬁnd it. Either of the above is a waste of time and makes
work less eﬃcient. For example, a job-hunting student needs
to write application forms for diﬀerent companies, for eachcompany, the application form is often not completed in one
Figure 1 Example of recommendation

go and needs to be ﬁnished multiple times. When writing
the application form for Company A, this student referred
to other people’s application forms on the job-related website. There is a number of application form examples for thesame company on job-related websites, such as some popular
companies with as many as 2,975 (as of this date, 6 January2023). When the student wants to continue writing an application form for company A for the second time, the student
has to search through 2975 examples browsing history to ﬁnd
the one wanted, either of which is time-consuming and may
not be found. This study will be used for the web browsing
history of the chrome browser on the computer to investigate recommendations for revisiting web pages that have areference value to the web pages currently being viewed.
The aim of this study is to recommend pages to users thatthey can revisit based on the pages they are viewing. Morethan 45% of the pages that we visit on the web are pages that
we have visited before [12]. So what kind of pages are considered necessary to revisit? Let’s assume that a student tried
to do a programming assignment, and searched for some coding grammar. He or she browsed dozens of pages including
the following. She browsed the following pages.
•

The SERP for the query “pandas view rows speciﬁed

column”.
•

The page for pandas:

select rows/columns indataFrame.
•

The SERP for the query “Python recursive functions”.

•

The page for understanding the Python recursive func-

tions.

ods that modeled user behavior in a one-way fashion from

After the ﬁrst task is ﬁnished, there is another task doing
a machine learning-related assignment. She looked up somecode for processing data and machine learning models andbrowsed the following pages.
•

The SERP for the query “Least squares method

left to right and employ deep bidirectional self-attention tomodel user behavior sequences.
2. 2.

Web Page Revisitation

Browsers support revisits with various tools, including
bookmarks, history views and URL auto-completion. How-

Python”.

ever, these tools only support revisits to a small number of•

The page for Least Squares Regression in Python.

frequently and recently visited pages. Several browser plug-•

The SERP for the query “machine learning dataset

ins and extensions have been proposed to better support the

split”.
•

long tail of less frequently visited pages, using recommendaThe page for Train-Test Split for Evaluating Machine

Learning Algorithms.

tion and prediction techniques [12]. A work in [18] constructs
a predictive model to determine whether a website will be

In both tasks, she may have been writing code in the editor

revisited by a particular user, the model can be used to ﬁl-while referring to these pages. Although tasks are diﬀerent

ter web records to only present revisiting web pages. Studyand queries are not the same, there are still some connectionsin [11] presents that the term page revisit had to be diﬀeren-

between the two assignments. For example, there istiated, and also identiﬁes diﬀerent types of revisitation that•

Both are programming tasks.

allow assessing the quality of current user support and devel-•

Both are related to “python” code.

oping concepts for new tools. A work in [7] leverages human•

Pandas is available for splitting datasets.

natural recall processes, and proposed a personal web revisit-

In this study, the pages browsed in the programming task

ing technique through contextual and semantic memory cuesand the machine learning task is considered to be related.

to facilitate recall. They discussed underlying techniques for

When doing the second assignment, the pages browsed incontext and content memories’ acquisition, storage, decay,

the programming task have some reference value to another.

and utilization for page re-ﬁnding, also have added a rele-

In this case, the results page of the programming task canvant feedback mechanism to accommodate individual mem-

be recommended as a revisited page.

ory strength and revisiting habits.

The diﬃculty in recommending such revisitable web pages

The problem of the next-page prediction has been exten-

lies in ﬁguring out the conditions under which pages are ef-

sively studied in the literature. The method that has pre-fective for the user’s ongoing web browsing behavior.

vailed in this ﬁeld, at least in terms of popularity, is Association Rules Mining. Association rules (AR) constitute a2.

Related Workwell-established method for eﬀectively identifying related re-In the ﬁrst part of this section, we summarize the ﬁndings

sources without taking into account their order of appearance

from several studies on existing web page recommendations.

(e.g., pages that are typically visited together, in the same

In the second part, we analyzed some studies on web page

session, but not necessarily in the same order) [3, 4]. Nu-

revisitation. Finally, we discussed some web usage related

merous works have investigated the performance of diﬀerent

research also including URLs studies.

variations of AR [2, 23, 37, 13, 31].

2. 1.

Web Page Recommendation

The dominant approach to predicting revistation, at least

SASRec model [8] predicts the next items by seeking toin terms of popularity, is association rule mining. Associa-

identify which items are relevant from a user’s action his-tion rules (AR) are a well-established technique for eﬃciently

tory based on self-attention. Top-N sequential recommenda-

identifying related resources regardless of the order in whichtion [16] predicts the top-N ranked items that a user will

they appear [2]. Several studies have examined the perfor-

likely interact with next by modeling each user as a se-mance of diﬀerent variants of association rules [10] [3].

quence of items that interacted with in the past. Real-life

2. 3.

Web Usage

recommendation systems often base only on short session-

One of the earliest studies on Web usage behavior was inbased data instead of long user histories, Bal´azs Hidas [6]1995 [17]. Tauscher and Greenberg found that 58% of an in-

is propose an RNNbased approach for session-based recom-

dividual s pages are revisits and Web users often carried out

mendations in which more accurate recommendations canrecurrent tasks on the Web. Adar [1]investigated revisiting

be provided. Modeling the dynamic preferences of users isbehavior by using a large user base collected via the Win-also important and challenging for recommendation systems.

dows Live Toolbar. It was found that short-term browsing

BERT4Rec [15] improves on previous recommendation meth-included basic browsing, visits to shopping or reference sites
or information tracking pages. Medium-term visits includedpopular homepages, web mail, forums, educational pages,
and browser homepages. Long-term visits included searchengines and weekend activities, such as going to the movies.
URLs are also an important part of web usage and we
discuss some URLs studies as well.

Several studies de-tect malicious URLs using machine learning.

Tradition-

ally, Malicious URLs detection is done through the usageof blacklists, which cannot be exhaustive and cannot detect newly generated malicious URLs. URLNet [9] applies
Convolutional Neural Networks to characters and words in
URL strings, capturing several types of semantic information in URLs. R. Vinayakumaras [13] evaluate various deeplearning architectures speciﬁcally Recurrent Neural Network(RNN), Identity-Recurrent Neural Network (I-RNN), Long
Figure 2 Repetitive task example

Short-Term Memory (LSTM), Convolution Neural Network(CNN), and Convolutional Neural Network-Long ShortTerm memory (CNN-LSTM) architectures by modeling theof the homepage of Company A, we aim for web pages withreal known benign and malicious URLs in character-level lan-members of Company A’s reviews and web pages with appli-guage, ﬁnally, ﬁnd out LSTM and the hybrid network ofcation form examples about Company A.

CNN and LSTM performance the best. URLdeepDetect [14]3. 1. 1.

Currently browsing web pages

proposes a hybrid deep-learning approach for time-of-click

In this study, the pages with the newest records of the

URL analysis and classiﬁcation to detect malicious URLs,

browsing history are deﬁned as the currently browsing web

also, determine a given URL as either malicious or benign

pages.

by analyzing semantic and lexical features of a URL by ap-

3. 1. 2.

plying various techniques, including semantic vector models

This study argues that people use computers to process

and URL encryption. The PC Log used in this study is therepetitive tasks, that when Currently Browsing web Pages

browser’s web browsing history, with the main object being

are similar or related to tasks that have occurred in the past,

the URL. Meanwhile, deep learning is also applied in the

the Currently Browsing web Pages and a speciﬁc past session

analysis of the system log.

are deﬁned as related sessions. The Revisiting web Pages

Just as the detection of malicious URLs is helpful for users
to use web services safely, the detection of anomalies in sys-

Repetitive tasks

should be in the browsing history, and also in the relevant
session.

tem logs is a key step to resuming a secure and trustworthy

3. 1. 3.

system. DeepLog [5] is a deep neural network model that uti-

To describe what revisiting pages are, it’s necessary to de-

lized Long Short Term Memory (LSTM), models system logs

scribe the relationship between the signature web activities

as natural language sequences and detects anomalies whenin the repetitive tasks and the revisiting page as shown in

the log content deviates from the normal straight-line direc-

Figure 3.

tion by learning the normally executed log content.

Revisiting web pages

Let us assume that a user planned to go to a 3-day-lastmusic festival. The user’s favourite singer G would give a

3.

Preparation for Revisiting web Pages

performance at the music festival, but he didn’t know which
day it was. So the user found singer G by processing the

3. 1.

Problem Deﬁnition

This research addresses the problem of recommending revisiting web pages from the currently browsing web pages.
The inputs in this study are the most recently viewed web
pages. The outputs are worth revisiting web pages. The
worth revisiting web pages are supposed to be a reference tothe most recently viewed pages.
Input Currently Browsing web Pages
Output Revisiting web Pages
For example, as shown in ﬁgure 1, if input the sub-pages

schedule page. After a while, he was not sure which day his
favourite artist would come, so he or she opened the festival website again. This time the user found singer G in thelist of performers and then access the page with the order ofthe singer’s appearances. Twice the user’s web activities areshown in the ﬁgure 4.
The parts without marks are not the same, but twice of the
web activities both start as parts green marked and end withparts yellow marked. Although the user processed singer G’sschedule in diﬀerent ways, the starting activities and ending

Figure 5 Chrome web Browsing History

Figure 3 Revisiting web pages example
Figure 6 Browsing History with session id

search results P1, and clicks on it, then the from id of P1 willbe the id of the google search restaurant page. In this example, the google search restaurant page opens directly and is
not clicked from another page, so its from id is 0. After the
Figure 4 User’s web activities

pre-processing of the dataset, 108,190 web browsing histories
were obtained for this study.

targets are the same.
Actions such as green, are called the signature web activities in the repetitive tasks. Target pages, like yellow, whichis always reached after the signature web activities, is deﬁned as revisiting pages. The method we propose is to skip
the search process and go directly from the signature web
activities to the revisiting pages.
3. 2.

Revisiting Candidate Data

As described in 3.1.3, we collected pairs of data from thesignature web activities and revisiting pages. In this section, we will discuss the metadata used and the processing

browser usage, we do this to ensure that as many pages
viewed on the computer as possible were collected over theentire period. The data is collected in seven months from
April 2022 to January 2023.
3. 2. 2.

Session splitting

The purpose of splitting a session is to split the huge browsing history into several collections, using the collection of
web pages to represent a particular task that the user is concentrating on. In this study, a session is considered to haveended when no new pages are opened for more than two min-methods.
3. 2. 1.

During the data collection period, chrome was set as thedefault browser to ensure that there was no diversion ofBrowsing history

The data used in this study is the web browsing history of
the chrome browser. There is a number of data in the browse
history database, we use only ﬁve of those data as follow.
•

id

•

url

•

title

•

visit time•from id

The id is the default setting in chrome, each id appears
only once and increases by 1 in chronological order. The
URL and title are obviously the URL and title of the page
being viewed.
The visit time is the time when the page was opened. The
from id means that a page was opened by clicking on a linkfrom another page. It should be 0 or less than the id of thebrowsing record itself. For example, if the SERPs after asearch for a restaurant on google select one of the returned

utes. Pages that are used in the same session are considered
to have the same purpose, for example, to process the sametask.
We calculate the time interval between each of the two adjacent historical visits to the web page by using Visit Time,
split the session by determining whether the time interval isgreater than 2 minutes, and attach the session id information. Add one after the other in chronological order. Finallygot 5083 sessions in total.
3. 2. 3.

Collecting Revisiting Candidate Data

In this section, we will describe what revisiting candidates
are and how to ﬁnd them in each session.
As showing in Figure 7, These are sessions with a timeinterval, and it can be known from the browsing history on
January 15 that the user was doing something related to job
hunting when this session occurred. After visiting the homepage of company A and the subpages in the homepage, theuser opened the job hunting related website and searched forTable 1 Sample of Revisiting Candidate Data
ActiontargetLearning long-termLong-term transaction - Google Searchdependencies in NARX
recurrent neural networks
Training, Validation,

python dataset splitting - Google Search[SEP] Evaluation and data set partitioning

and Testing with BERT｜
Sentence Classiﬁcation
with BERT and Transformer

Company A - Google Search[SEP] Information of Company A

Example of entry
sheet for Company A

Figure 7 Collecting revisiting candidate

company A, trying to ﬁnd some reference information, andﬁnally reached the page of example entry sheet b.
It is important to note that there are many subpages in
the company homepage and job search related websites, especially when searching for a company in job hunting related
websites, just like search engines, the reference content returned may not be the same every time, and it takes time
to ﬁnd what you can refer to among hundreds of es example
entry sheets.
The browsing history on January 18 shows that the user
searched for company A on the job hunting related website
after visiting company A’s homepage, and again visiting page
A. In the two visit records, the parts marked in red are thecompany homepage, the company information page, the job
Figure 8 Method of revisiting recommendation

hunting related website and the content of company A on
the job hunting related website. These four pairs of pages
are called revisiting candidates, also the last one in the timeby calculating the similarity between the title of currently

series is Target, eg.Example entry sheet a, and the others arebrowsing pages and the signature web activity pages. In thiscalled Action, eg. Homepage of Company A, Information ofsection, we will discuss the process of similarity calculation

Company A, Job Hunting Related Site N.

and revisiting recommendation.

We collected 216 pairs of revisiting candidate using the

4. 1.

Recommendation Interface

approach described above, some examples of revisiting can-To implement revisiting web pages based on the web pages

didate data we collected in this study are shown in Table 1.

being viewed, the recommendations need to react timely. A
simple recommendation interface is constructed to prompt

4.

Revisiting Recommendation

As showing in Figure 8, we recommend revisiting pages

the user for recommendations. As shown in Figure 9, theinterface is linked to a database of chrome browsing history,

and when the “search” button is pressed, the latest browsing history will be obtained for analysis. The recommended
results will be output on the right side of the interface andpressing the “Open” button to open all.

Figure 10 recommendation interface

not have the same words or even words with similar meanings. Also among the returned results containing related andsimilar words, it shows that the top results do not actuallycontain revisited pages that can be used. The results of calculating the similarity of texts are not satisfactory, so we
tried to collect more data and try to get more accurate results by having the model learn the data from the relevant
sessions of the positive solution.
Figure 9 recommendation interface

4. 3.

Revisiting Page Recommendation

In this section, We will use similarity calculations to recom4. 2.

Vectorization of Title

Bidirectional Encoder Representations from Transformers(BERT) is a family of masked-language models publishedin 2018 by researchers at Google [4]. We used BERT wordembedding to vectorize the title text, trying to get relevant
sessions by cosine similarity calculations and recommending
suitable pages. The steps are as follows:•

Keep only the title text of the page•

Recurring title elements are retained only once•

Combine the title elements into one line like a sentencewith spaces•

Tokenize texts in each session– Get the session format for vectorization•

Vectorise each session•

Use cosine similarity to ﬁnd the similarity between in-dividual sessions
In this section, we construct a simple search system. The
query is for a particular session and returns the top5 sessions
with a high degree of similarity to it.
The returned session will basically have multiple occurrences of the words in the query or related words, but some
do not have the same words or even words with similar meanings.
The returned sessions basically have multiple occurrencesof the words in the query or related words, but some do

mend revisited pages based on the page being viewed. The
ﬂowchart of recommending the revisiting pages is shown in
Figure 8.
The data used in this study was extracted from the browsing history database of chrome on the computer. Whenevera user opens a new web page, the database is updated witha new log. Therefore, for the pages being viewed, we use the
5 most recent recommendations for revisiting this databasein chrome browsing. The data is just extracted one by one
as shown in the ﬁgure. First, we format the output for theﬁve latest logs and use the title of them. For example, thecurrently browsing web pages in Figure 8 are ﬁnally linked
together like a single sentence. We treated the title as an
input sentence.
Then, we calculate the similarity between currently viewing web pages and each of the Actions of revisiting candidate data. After generating drew the feature vectors of thecurrently browsing web pages and actions of revisiting candidates being visited, calculate the cosine similarity between
the two vectors. From this, we can get the similarity between
the currently browsing web pages and each action of revisiting candidates, to get a descending ranking of the revisiting
candidates based on this similarity score. In this study, we
work on ﬁnding browsing behaviors with the same purpose.
And the targets in revisiting candidates with high similarity
ranking are seen as useful web pages to the currently brows-

Figure 11 Revisiting Recommendation for Programming Task
Figure 12 Revisiting Recommendation for Job Hunting Task

ing web pages. Therefore, it can also be said that the targetsof revisiting candidates with high similarity can be recom-

6.

Conclusion

mended as revisiting web pages because they have the same
purpose. Based on the ranking of the revisiting candidates

This paper proposes a system to automatically recommend

generated above, the target of revisiting candidates is output

revisiting web pages based on currently browsing pages. Re-

as revisiting web pages recommendation to the user.

visiting web pages should be included in the pages that theuser has visited in the past. As for the research process,5.

Evaluation

In this section, we will examine several speciﬁc output examples of revisiting web pages recommendation.
Figure 11 shows a sample of revisiting web pages recommended by our systems, when searching for some coding
problems in the programming task.
The programming task is for searching a coding problem,the approach of the segmentation dataset. All of the top3
recommended web pages are from past browsing history,
and are relevant to dataset segmentation and pre-processing.
Each of them is informative about the aim of the task. However, due to the limitation of training data, very few revisited
pages could be recommended.
Figure 12 shows another sample of revisiting recommendations for job-hunting tasks.
The job-hunting task is for submitting Entry sheets toapply to companies, by referring to the intelligence on thecompany’s oﬃcial website and series of job-related websites.
When the current browsing reaches the home page of Company A, the top3 revisiting web pages recommended by oursystems are all very related to Company A on job-hunting
websites. However, only pages that have been visited in thepast can be recommended, so the range of job-related websites is very narrow.

we ﬁrst deﬁned signature web activities and revisiting pages.
The recommendation for revisiting web pages consists of two
steps. First, we collect revisiting candidate data based on thedeﬁnition of signature web activities and revisiting pages.
Then, by calculating the cosine similarity between currently
browsing web pages and signature web activities, we obtaina ranking of recommending revisiting web pages. Finally,
We evaluate our methods through some case studies. We
found that this method currently suﬀers from limited revisited candidate web pages. The results are very informativein the face of revisiting behavior with a high degree of similarity. However, because the candidate revisiting data islimited, for most currently browsing web pages that do nothave a high degree of similarity, the recommendation results
are not informative for pages being viewed that are not very
similar.
We consider making a more convincing evaluation in the
future.
•Evaluation of diﬀerent pre-treatment methods.

– Try other time intervals and observe the ﬁnal results
obtained for each time interval. The current threshold of
120s is artiﬁcially determined, which can actually be morescientiﬁcally and logically set to a value. For example, thebar chart of the time interval can be analyzed to have a numerical understanding of the end time of each network accessbehavior by observing the trough values roughly.
•

Evaluate the output of revisiting pages.

[4]– For this part of the evaluation, we are considering using the last month of the browsing history data set for theevaluation, and use the data from the very beginning to the[5]

beginning of the last month to give the model for learning.
The data pre-processing of the validation set is still ﬁne as it
is now, but the data of the evaluation set needs to be moreﬁnely divided into sessions, and the selection of targets, i.e.,[6]

the production of the positive solution data needs to be improved. In order to ensure the accuracy of the validation
set data, it can be produced manually. Finally, the perfor-[7]

mance of the system on revisiting web page recommendations
is evaluated by verifying whether the system’s revisiting recommendations for the last month of web pages are consistent[8]

with the prepared positive solution data targets.
•Conducting user experiments.

[9]– We are considering to asking multiple experimenters
to use the system and experimenters, and also asked the experimenters to conduct a questionnaire on the feeling of using[10]

the system and the usefulness of the recommended results.
In this, what is not clear yet is the overall system impression.

[11]

For example, should our system be an interface resident on
the browser, or can we ﬁnd the right time to pop up recommendations by automatically determining which web page
the user is currently browsing. The focus of these two types[12]

and the overall sense of the use of the system will be very different, in future work, we are committed to ﬁrst determining
the shape of the system, a more speciﬁc design of the overall[13]

deﬁnition and approach.
Future work includes but is not limited to improving accuracy by improving the existing recommendation system, or[14]

by more detailed pre-processing of web page browsing logs
(e.g., segmenting sessions). We are also considering adding
other computer logs as web activities to make more possibil-[15]

ities for revisiting recommendations.

Acknowledgements
This work was supported by JSPS KAKENHI Grant Numbers JP21H03775, JP21H03774, and JP21H03554.

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