Project report
IIT Bombay + Microsoft Research


A smarter context based phonebook for Mobile Devices

Guide : Prof. Anirudha Joshi

The Phonebook application is fairly ubiquitous and enables what appears to be a small but important function of a phone, the function of enabling communication. Despite this importance, our phonebooks have not really improved drastically. Smartphones with good computing capabilities have become fairly ubiquitous. These devices have the potential of collecting and storing large amounts of data about contacts, patterns of communication and other types of metadata such as location, context of the user and contact, relationship, anniversaries, etc. In a number of cases, smartphones already store this data. The ‘PhonBk’ application tries to embed intelligence in the phonebook by means of suggestions and enables novel ways of interacting with our contacts.

These days, it is normal to have over 1000 contacts in a phonebook which include phone numbers, emails and other data. We are in touch with some of these contacts frequently. We use many others rarely. The current visualisation of the phonebook data is quite old, and is essentially based on a feature phone. This project is about creating a solution that makes use of high resolution touchscreens and sensors in mobile devices to enhance the user experience. The term ‘Mobile devices’ has been loosely defined. The end solution is the design of a smarter phonebook that provides information about the most important contacts.

What is the most important contact right now?

Design Problem

The design of the phonebook has largely remained the same over the past years. Navigation and finding contacts is still based on Alphabetical Lists, Favorites, Call Logs and in some instances frequency based defaults. Our devices have become exponentially smarter, but not our phonebooks.

The following page shows different ideas and strategies that have the potential for making the Phonebook smarter. The ideas range from design of the User Interface to Designing algorithms, using machine learning techniques to solving navigation and User Experience problems.

Pareto Principle : 80/20 Rule

The Pareto principle states that out of all the available information, only about 20% of it is valuable. A number of studies suggest this to be true even in the case of contact management. Out of all the contacts in the phonebook, only a handful of contacts are important at any given point of time. This number generally ranges from 10 -20 contacts, however different studies suggest a different number. Overall, only around 20% of the contacts are important for the average person.

Contact Information

The smartphone has the potential of storing large amount of metadata for each contact as shown in the figure below. This opens a range of possibilities in design, prediction, learning and a number of other fields. The questions we then need to then ask are ethical rather than technical.

How to decide whether a contact is important ?


Recency and Longevity

The more recent the interaction, the more long term the interaction, the more important is the contact. Recency may indicate temporary importance while longevity indicates certain importance.

Bayesian Networks

The importance of a particular contact may also depend on the current context of the user. Some contacts are important in certain locations, at certain times, often a combination of both. The importance may also depend on the closeness or relatedness of a contact to another contact. All this information can often be coupled with recency and longevity patterns to find out who is important to me right now. This data can be used to predict the most important contacts and visualize the same for the phonebook.

The following figure indicates different types of contacts that may be important at different time durations in a 24 hour time span for a typical office goer.

How do people store contact information? – User Studies

To find out patterns in contact storage, a user study was conducted with a group of users. There were certain repeating patterns found in the contacts storage. These were thought to be not very useful in the beginning. Different users stored the same contact with different names and information. One can however generate groups from the semantic structure of different contact names; an idea that will be explored later in the project.

Proposed solution – PhonBk

The following diagram describes the information architecture of the PhonBk intervention. The solution maintains the structure of the original phonebook application while including design interventions for finding relevant contacts quickly and easily.

Predicting the next important contact (Suggestions)

This idea derives inspiration from the Phoneman paper where you predict the next possible action after the current action. For example, after using the phonebook the next action would probably be using the calendar or creating a reminder. In similar fashion, the contact data reveals patterns in calling certain individuals. After calling a specific person, I am prone to make the next call / interaction with a specific group of people. This data can be used to predict and suggest the next possible contact.

Recommendation Panel

The recommendation screen is the first encounter with the application. It consists of the following elements :


It was found in the user studies that favorites is not a frequently used function. Users on an average saved 0-5 contacts as favorites. Most users did not make use of the favorites functionality. The favorites tab is thus designed to occupy a smaller screen real estate.


The suggestions panel occupies 2/3rd’s of the screen space. The suggestions panel consists of 10 contact elements. The elements show indicative tags for why a particular contact has been suggested (indicative tags for birthday’s, anniversaries, meetings, etc).

Favorites and Suggestions are one of the three major components of PhonBk app for wearables apart from search.

Finding contacts based on past experience

How many times does it happen that you are trying to find a contact you saved (or worse, you did not save!), but you forget the name. You probably know other information like when had you called the contact, where you were when you called, who else did you call. In short, you know the context of the call. This idea proposes an additional navigation of the phonebook by means of filters (date, time, people, location, day).

The solution proposed is an advanced filter. Additional filters of location data may be added. The way in which a contact is found would depend on the type of user. It is very well possible that a user scrolls endlessly till he finds the needed contact. However I believe that even though simple things should be simple, complex actions need to be possible.

Contact List

The contact list consists of two modes, mainly the ContactGrid Mode and the Standard List mode. The Standard List is an alphabetically arranged list of contacts.

Contacts Grid

A friend of mine with a generic name (Akshay, Sagar, Prasad etc.) recently told me that whenever his father wants to call him he has to type the son’s name completely since there are a lot of people with the same name. The following idea germinates from this problem. It consists of a simple grid of alphabets for navigation. On selecting a particular alphabet, the screen shows a grid of people with names starting (this is given preference) or containing the alphabet with the alphabet. The size of each element in the grid is determined by the importance of the contact based of frequency, recency and longevity patterns discussed earlier.

This is the default mode where the interaction begins with a grid of alphabets. When the user taps on an alphabet a schematic treemap view of the top 10 contacts for quick access starting with the selected letter is shown. The location and size of the treemap grid remains constant despite changes in the calling frequency. This is to ensure that the schematic grid is imbibed in the user’s muscle memory. A constant change in size and position of top contacts might lead to ambiguity and confusion. Scrolling below reveals an alphabetically arranged list.

PhonBk for Wear

The Suggestions feature of the phonebook along with favorites is a good solution for quick access of important contacts for mobile devices with a medium to large screen real estate available. However, these features become even more useful in cases where the screen real estate is limited and the user is not always able to access the entire Phonebook. A wearable device in the form of a smartwatch is a preferred use case for these features. A smartwatch does not have enough screen real estate for users to scroll or search through an entire phonebook with more than 1000 contacts.

5 Second Rule

The design principles for android wear suggest, that any action on the wearable device should not require the user more than 5 seconds to perform. A typical use of a wearable app is less than 5 seconds. Hence, the PhonBk App for Android wear consists on only 3 functions for quick access of relevant contacts i.e. Suggestions, Favorites and Voice Search.

Project Report

Link to the project thesis report


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