Context completion auto-suggestor for real estate domain like 99acres.com

1. Context completion suggestor for
real estate domain
Dhwaj Raj
Member
Web Intelligence and Semantics (WISE) group
InfoEdge India Ltd.

2. Outline

Aim

Functionalities

Technical Aspects

3. Background

Current keyword queries were analysed from tracking db.
Following are the TOP entity types that were being typed in by user even
after providing the sufficient filters:

PROJECT

LOCALITY

CITY

BEDROOM

PROP_TYPE (flat, villa etc)

BUILDER

PREFERENCE (rent, sale etc)

AVAILABILITY (ready to move etc)

FURNISH (fully furnished etc)

TRANSACTION TYPE (resale etc)


Zero result count and pattern were analysed for keyword queries
The language of Google SEM queries was analysed to form langauge
for our entity combinations.

4. Aim

What this autosuggestor is NOT :
I just started to type “fla” in the
keyword box.
Why is it not suggesting “2bhk flat in
cochin near coconut farm”
Mind Reading Device
Why is it not suggesting “champak
town” of timbaktoo
Magic Box to generate content
Why only 10 suggestions? User will
think we have only 10 properties.
Not for imbecile.
Tried asking google
the
same queries ???

5. Aim

Focus :
−
−
Typing
Experience

Typing Ease

Correcting Mistakes

GUIDE user to how to find useful information

Let them type MULTIPLE ENTITIES

Just Browsing? Find other useful information

6. EASE OF TYPING



Words are suggested for AUTOCOMPLETE.
Context completion : Phrases are suggested for SUGGESTION
PICKING.
User can choose from predicted searches, which makes finding
information faster and requires less typing.

7. CORRECTING MISTAKES

8. GUIDING USER TO WRITE
BETTER QUERIES

9. MULTIPLE ENTITIES

10. FINDING OTHER USEFUL
INFORMATION

Many a time user is not sure or focused on a single search. In such
cases autosuggestor help him navigate to the popular properties which
he was not even aware of.

12. Investigations
The entities with higher co-occurence in property listings have higher chances to be typed
in together.
Real word queries can be simulated by combining entities to form norrowed down phrase
queries.
The property listing trends and property availability correlate to query popularity for similar
properties.
The competitions among matched phrases can be resolved by weighted rank of entity
cooccurances, entity type combination weights and feedback.
The user experience can be improved by a hybrid auto suggestor to suggest words,
phrases, full-forme-queries and spelling mistakes.
Iff query picked from suggestor then we wont need token mapping or QER as we will be
maintaing the entities present in a phrase at backend

13. Technical Implementation

14. Thank you