1.
TABLOID
Submitted to: Submitted By:
Mr. Sandeep Kumar Singh Shivam Prakash 10103609
Ms. Parul Agarwal Lohitaksh Varshney 10103599

2.
Introduction
• Tabloid is a content aggregation service. It provides the aggregation of RSS
feeds, as well as aggregation of well-known blogs and websites built into
the service. Content can be separated into categories and can be marked
read or unread.
• An RSS feed provides a method for websites to publish information once
and syndicate it automatically amongst millions of subscribers. An
aggregator captures, and organizes, feeds to simplify the consumption of
news. No longer do we need to constantly revisit the same site, wondering
what’s new. Now we only need to add RSS feeds to our reader, also known
as aggregators, and wait for the news to come to us. Readers provide
information consumers with huge advantages
• Tabloid has a variety of experiences, but for the most part readers swipe
upwards to browse through more of a paginated experience. The UI is
richer and features more of a magazine style similar to Feedly and
Flipboard.

3.
Feature of Tabloid
• User can customize their own magazine according to their interest by subscription
• Content can be separated into categories.
• Example if the user likes news on car and related topic he can categorize it under their
own desired name such as automobile
• Subscriptions can be easily added or removed. User gets an option to preview its
contents before subscribing it.
• No longer do the user need to constantly revisit the same site, wondering what’s new.
Example if user vests interest in car he will be constantly getting updated about the news
related to it.
• Recommendation based on the users Facebook profile activities, which will suggest him
with various other relevant content that might interest him to overcome the cold start
problem in content based analysis
• Recommendations on the basis of the news feed read
• Recommendation on the basis of the article read

4.
Problem Statement
• The aim of the project is to create a web application for customization
of news from various sources and to recommend the users with the
contents the user would like.
• It should provide the aggregation of RSS feeds, as well as aggregation
of well-known blogs and websites built into the service. Content can
be separated into categories and can be marked.
• It should capture and organize feeds to simplify the consumption of
news. No longer do the user need to constantly revisit the same site,
wondering what’s new. Now the user only need to add RSS feeds to
the website, and wait for the news to come to their subscription.

5.
Benefits of the application
• Simplified user interface where the user can easily find news and feed
by searching for it
• User can review the newspaper or feeds before subscribing it
• Applications recommendation system work automatically to generate
the best relevant news and feed that the user would like read
• Application removes repetition of similar news/feeds from various
subscriptions by selecting the best one from it

6.
Comparison with present technologies
Name of the tool Description of the tool Weakness of the tool
Feedly Feedly is a news aggregator application for various Web
browsers and mobile devices running iOS and Android,
also available as a cloud-based service
 No search. Can't search through old read/unread
feed content like you could with Google Reader.
 The same news repeats from different subscriptions
 Recommendation is slow as there is not previous
detail of the new customer
Flipboard Flipboard is a personalized magazine app designed for
phones and tablets, but you can access it on a PC, too. It
takes stories from around the web based on your own
interests and delivers them to you in an attractive visual
feed
 The same news repeats from different subscriptions
 Recommendation is slow as there is not previous
detail of the new customer
Digital newsstand A digital newsstand is a digital distribution platform for
downloadable newspapers and magazines
 UI is not interactive
 No Recommendation System used

7.
Tools Used
• Gensim is used to extract semantic topics from documents. For this a corpus is made which is
used to infer their topic. Then the status fetched previously are used to compare with this corpus
using lda and accordingly a topic is assigned to the status. Now we find the users interest on the
basis of the topic of his status. We can then use this information to get other users with similar
interest.
• Pygoogle : pygoogle is a very basic Google search module for Python. It has a limitation of only
64 results at present we are fetching the top five results
• NLTK is a leading platform for building Python programs to work with human language data. It
provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along
with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing,
and semantic reasoning
• Goose: It is used to extract article ,tagline and main image from the specified URL.
• Feedfinder: It’s a type of search engine which searches for RSS feeds starting at any toplevel URL
and crawls the whole site.

8.
• Beautiful soup: Beautiful Soup is a Python library for parsing HTML documents (including having
malformed mark-up, i.e. non-closed tags, so named after Tag soup). It creates a parse tree for
parsed pages that can be used to extract data from HTML, so this library is useful for web
scraping extracting data from websites.
• Flask : Flask is used to host local server on our desktop to recommend feed/articles from python
script and display it to the user on HTML.
API Used
• Clipped: Clipped analyses text grammatically to extract the most important information from an
article. The algorithm identifies sentence structures statistically, and graphically determines
predicate-subject relationships to generate the best summaries possible.
• Google RSS Feeder: The Google Feed API takes the pain out of developing mashups in JavaScript
because you can now mash up feeds using only a few lines of JavaScript, rather than dealing with
complex server-side proxies. This makes it easy to quickly integrate feeds on your website.
• RSS Feed Validator: This is a validator for syndicated feeds. To use it, simply enter the address of
your feed and click Validate. If the validator finds any problems in your feed, it will give you
messages for each type of problem and highlight where the problem first occurs in your feed

9.
Novelty
• Tabloid allows blog or website readers a single destination when checking
updates.
• User can customize their own magazine according to his interest by subscription
• Content can be separated into categories.
• Example if the user likes news on car and related topic he can categories it under
their own desired name such as automobile
• Subscriptions can be easily added or removed. User gets an option to preview its
contents before subscribing it.
• Recommendation based on the users Facebook profile activities, which will
suggest him with various other relevant content that might interest him
• Does not has a cold start problem as there will always be data as ones the user
sign-up the application will have access to all the data from the users profile

10.
Snapshots

11.
Implementation Issues
Problem 1
• A key issue with content-based filtering is whether the system is able
to learn user preferences from user's actions regarding one content
source and use them across other content types it’s a cold start
problem
Solution
The user is asked to register from his Facebook account so that a
dataset can be created from his status/comments

12.
Problem 2
To abstract the features of the items in the system generally tf–
idf representation (also called vector space representation) is used, but
in our case the results were not optimum
Solution
Semantic analysis was used as it does not involve prior semantic
understanding of the documents

13.
Problem 3
Comparisons for the document similarity was used to remove the
repetition of documents with the similar news.
Solution
LSA model was created for discovering the abstract "topics" that occur
in a collection of documents.

14.
Algorithms
Levenshtein distance
• The Levenshtein distance is a string metric for measuring the
difference between two sequences. Informally, the Levenshtein
distance between two words is the minimum number of single-
character edits (i.e. insertions, deletions or substitutions) required to
change one word into the other

15.
• Tf-idf
• Term frequency–inverse document frequency, is a numerical statistic
that is intended to reflect how important a word is to a document in a
collection or corpus. It is often used as a weighting factor
in information retrieval and text mining. The tf-idf value
increases proportionally to the number of times a word appears in
the document, but is offset by the frequency of the word in the
corpus, which helps to control for the fact that some words are
generally more common than others.

16.
• Cosine Similarity
• Cosine similarity is a measure of similarity between two vectors of
an inner product space that measures the cosine of the angle
between them. The cosine of 0° is 1, and it is less than 1 for any other
angle. It is thus a judgement of orientation and not magnitude: two
vectors with the same orientation have a Cosine similarity of 1, two
vectors at 90° have a similarity of 0, and two vectors diametrically
opposed have a similarity of -1, independent of their magnitude.
Cosine similarity is particularly used in positive space, where the
outcome is neatly bounded in [0,1].

17.
Latent semantic analysis (LSA)
• Latent semantic analysis (LSA) is a technique in natural language processing, in particular
in vectorial semantics, of analyzing relationships between a set of documents and the terms they
contain by producing a set of concepts related to the documents and terms. LSA assumes that
words that are close in meaning will occur in similar pieces of text. A matrix containing word
counts per paragraph (rows represent unique words and columns represent each paragraph) is
constructed from a large piece of text and a mathematical technique called singular value
decomposition (SVD) is used to reduce the number of columns while preserving the similarity
structure among rows. Words are then compared by taking the cosine of the angle between the
two vectors formed by any two rows. Values close to 1 represent very similar words while values
close to 0 represent very dissimilar words

18.
Limitations of the solution
• New feeds
• Tabloid has a feature to add the news feed by just simply inputting
the url of that feed in the search. The problems arises here when a
user types in the correct url it gets validated by an url validator api,
sometimes the correct url does not get validated, hence can cause
trouble to the user
News Feed searched as string are not added dynamically
• Once the text is searched, it does not updates immediately because
first it corresponding rss is searched then updated in the database

19.
Findings
• The latent structure approach is useful for helping people find textual
information in large collections. It helps overcome vocabulary
problems that pose severe limits on human-computer interaction by
automatically extracting underlying semantic factors. In terms of
helping users find objects of interest, the LSI approach compares
quite favorably with existing keyword-based methods, and suggests
some new possibilities for customization and selection.

20.
• Propositions typically represent semantic information at a clause
level, while LSA is more successful performing analyses at a sentence
or paragraph level. The few words in a clause make the vectors in LSA
highly dependent on the words used in that clause, while sentences
contain enough words to permit a vector that more accurately
captures the semantics of the sentence

21.
Conclusion
• Tabloid is a content aggregation service. It provides the aggregation of
RSS feeds, as well as aggregation of well-known blogs and websites
built into the service.
• With improved recommendation system it can stand out compaired
to other aggregation services.
• No longer do the user need to constantly revisit the same site,
wondering what’s new.
• Subscriptions can be easily added or removed. User gets an option to
preview its contents before subscribing it.

22.
Future Work
• Expand this for other social networking websites such as Twitter,
LinkedIn etc to improve the cold start problem .
• Improve the efficiency of the algorithm for more accurate and faster
results

23.
Thank You