User Behavior-Driven Query Expansion Techniques

min. read

September 11, 2024

User Behavior-Driven Query Expansion Techniques

Query expansion uses user behavior to improve search results. Here's what you need to know:

It adds related terms to search queries based on user actions
Analyzes clicks, time on page, and search rephrasing
Uses machine learning and real-time data for better accuracy

Key benefits:

Bridges gap between user queries and intent
Improves search relevance and user satisfaction
Works across e-commerce, research, and social media

Challenges:

Data scarcity for new/niche topics
Balancing personalization and general results
Potential for user data bias

Future trends:

More context-aware expansions
Instant, predictive query suggestions
Multi-language and multi-format capabilities

Method	How it Works	Impact
Indirect feedback	Analyzes clicks and time on page	Decodes search intent
Direct feedback	Uses ratings and query rewrites	Improves result relevance
Session analysis	Examines entire search journey	Connects related topics

Bottom line: User behavior-driven query expansion makes searches smarter, faster, and more accurate across various fields.

2. User Search Behavior

2.1 Types of User Data

Search engines are data-hungry beasts. They gobble up all sorts of user info to make query expansion better. Here's what they're after:

Data Type	What It Is	Real-Life Example
Search history	Your past searches and clicks	You can't stop Googling "python programming"
Click patterns	Which results you actually pick	You always click on coding tutorials when searching "python"
Session data	Your search journey in one sitting	You start with "python basics" and end up at "advanced python techniques"
Time on page	How long you hang around	You spend ages on that Python tutorial page
Location	Where you're searching from	You're in San Francisco looking for "python jobs"
Device type	What you're searching on	You use your phone for quick "python syntax" checks

2.2 Collecting and Using User Data

How do search engines get their hands on all this data? They use:

Cookies and tracking pixels
User accounts (like your Google account)
IP address tracking
Browser fingerprinting

And what do they do with it? They:

Make your search results personal
Get better at understanding what you're asking
Tweak their ranking algorithms
Come up with new features

Take Google's RankBrain, for example. It uses how people behave to figure out what searches really mean.

2.3 Privacy and Ethics

But here's the thing: all this data collection raises some eyebrows. People worry about:

How long the data sticks around
Whether users actually agreed to all this
Who else gets their hands on the data
If the government can peek at it

Search engines try to calm these fears with:

What They Do	What It Means
Data anonymization	They scrub out anything that could ID you personally
User controls	You can see and zap your search history
Encryption	They lock up your data tight
Transparency reports	They spill the beans on government data requests

But it's not all smooth sailing. The tech world is still wrestling with how to balance personalization and privacy.

3. User-Based Query Expansion Methods

Search engines use clever tricks to improve searches based on user behavior. Let's dive into three key methods:

3.1 Indirect User Feedback Methods

These methods silently watch what users do. They focus on:

Clicks: Which results users choose
Time on page: How long users stick around

Take Google's RankBrain. It uses these signals to decode search intent. If "python" searchers click on coding tutorials and stay a while, RankBrain learns it's likely about programming, not snakes.

3.2 Direct User Feedback Methods

Sometimes, search engines just ask users directly. This includes:

Ratings: Users score result helpfulness
Query rewrites: Users change their search terms

An Encarta search engine study found query rewrites super helpful. Check this out:

Original query	Rewrite query	Times rewritten	Users who did it
mcdonald's	burger	573	535
Bubble tea	koi	293	287

Users often switch between brands and general terms. "mcdonald's" becomes "burger", while "Bubble tea" turns into "koi" (a bubble tea brand).

3.3 Search Session Analysis

This method looks at a user's entire search journey. It helps search engines connect the dots between related searches.

Picture this: A user starts with "python basics" and ends up at "advanced python techniques". The search engine now knows these topics are linked and can use this info to help future searchers.

The Encarta study showed just how powerful these user-based methods can be:

Method	Improvement in search accuracy
User log-based expansion	75.42%
Traditional expansion	26.24%

The numbers don't lie - paying attention to user behavior leads to WAY better search results.

4. Query Expansion Algorithms and Models

Search engines use smart algorithms to boost results based on user behavior. Let's dive into three key approaches:

4.1 Machine Learning Methods

Google's RankBrain, launched in 2015, is a prime example. It uses AI to connect words with concepts.

Say you search for "what's the title of the consumer at the highest level of a food chain". RankBrain figures out you're asking about apex predators. How? By analyzing millions of searches and clicks.

4.2 Deep Learning Techniques

In 2018, Google rolled out neural matching. This system uses neural networks to link queries with web pages.

Then came BERT in 2019. It's a language whiz that gets context like never before. Check this out:

Query	Before BERT	After BERT
"Can you get medicine for someone pharmacy"	General pharmacy info	Understood user wanted to know about picking up someone else's prescription

BERT catches the key parts, giving you more helpful results.

4.3 Combined Models

Google's latest AI, MUM (Multimodal Unified Model), mixes different methods for even better results.

Launched in May 2021, MUM can:

Handle 75 languages
Process text and images together
Tackle complex tasks

Imagine searching: "I've hiked Mt. Adams and now want to hike Mt. Fuji next fall, what should I do differently to prepare?"

MUM gets it. It knows you're comparing mountains, understands the locations, factors in seasons, and gives you specific prep advice.

This combo of language smarts, multi-format processing, and handling complex tasks? It's a game-changer for query expansion.

5. Measuring Query Expansion Success

Search engines use three main methods to check if query expansion is working:

5.1 Relevance Measures

These look at how well expanded queries meet user needs:

Precision: Relevant docs ÷ Total docs returned
Average Precision (AP): Measures relevance for ordered results
Normalized Discounted Cumulative Gain (nDCG): Compares to ideal outcome

Measure	Checks	Best For
Precision	Overall relevance	Grid results
AP	Ordered relevance	List results
nDCG	Ideal comparison	Query comparisons

5.2 User Satisfaction Measures

These track user happiness:

Click-through rate (CTR): Clicks ÷ Result impressions
Session abandonment rate: Users leaving without clicks

5.3 A/B Testing

A/B tests compare query expansion methods in three stages:

1. Design:

Set up test
Pick key metrics (1-3)
Choose guardrail metrics

2. In-flight:

Show new version to some users
Monitor guardrail metrics

3. Analysis:

Review results
Decide on effectiveness

"These companies recognize that while choice is great, we can do much more to make the experience positive and productive." - Raj De Datta, CEO and co-founder of Bloomreach.

Real-world results:

Canadian Tire: 20% conversion boost with Bloomreach Discovery
Annie Selke: 40% search revenue increase in 6 months using Bloomreach

6. Problems with User-Based Query Expansion

User-based query expansion isn't perfect. Here are three big issues:

6.1 Not Enough Data

New users or niche topics? You might hit a wall:

Weak suggestions
Off-target results
Missing the good stuff

Some systems mix user data with content analysis to fill the gaps.

6.2 Personal vs. General Results

It's a balancing act:

Too personal? You're in a bubble.
Too general? You miss the mark.

Social-based expansions (like Google's Autocomplete) tend to be more specific than content-based ones. That's good and bad, depending on what you need.

6.3 User Data Bias

User behavior can mess things up:

Bias	What It Means	Why It's a Problem
Popularity	Common searches win	Less variety
Misinformation	False ideas spread	Bad info keeps going
Cultural	Limited viewpoints	Narrow results

To fix this, we need smart algorithms that spot and correct these biases.

"The probability of two different users using the same keywords to describe the same things is less than 20%." - Furan's experiment

This shows why it's tough to make query expansions that work for everyone based on user behavior alone.

7. Real-World Examples

Let's see how user behavior-driven query expansion works in practice:

7.1 Online Shopping Searches

E-commerce giants use this tech to boost sales:

Amazon's "My-Mix"

Amazon's personalized shopping feed is a game-changer. In 2016:

53% of customers said Amazon had the best site experience
33% chose Amazon over others because of it
Over 50% found Amazon's search and filtering top-notch

Walmart's Redesign

Walmart.com's 2018 makeover paid off:

Online sales shot up 30%
They hit $10 billion in online sales the next fiscal year

Now, they show trending items based on location and suggest products tied to recent buys.

7.2 Academic Research Searches

Query expansion helps researchers find relevant papers faster:

"Interactive features such as query expansion can play a key role in supporting these tasks." - Business Information Review study

The tech uses language models to balance precision and recall in search strategies.

Social platforms use behavior data to suggest content:

Starbucks' Rewards Program

By analyzing purchase history, Starbucks personalizes offers. This approach generated nearly 50% of their revenue in 2020.

Nike's Personalized Approach

Nike Plus gives early access to new products based on user preferences. Their app, Nike Direct, boosts customer acquisition and retention by understanding user behavior.

Company	Feature	Result
Amazon	My-Mix	53% best site experience
Walmart	Personalized site	30% online sales increase
Starbucks	Rewards program	~50% of 2020 revenue
Nike	Nike Plus	Improved customer retention

These examples show how user behavior data can transform search experiences across industries.

8. Future of User-Based Query Expansion

8.1 Adding Context to Searches

Search engines are getting smarter about understanding what you really mean. They're using more than just your words - they're looking at where you are, what time it is, and what you've searched for recently.

Take Google's BERT algorithm. It's not just matching keywords anymore. If you search "can you get medicine for someone pharmacy", BERT gets that you're asking about picking up someone else's prescription.

8.2 Instant Query Expansion

Ever notice how search engines try to finish your thought as you type? That's instant query expansion, and it's getting better.

Google's Autocomplete is just the beginning. In the future, these suggestions will be much smarter, based on what you're likely trying to find.

8.3 Multi-Language and Multi-Format Expansion

Search engines are breaking down language barriers. This is huge for global businesses and people who speak multiple languages.

A University of Amsterdam study found that expanding searches across languages boosted results by up to 23% for Dutch-English searches.

Here's a quick look at where we are and where we're headed:

Expansion Type	Now	Future
Context-aware	Basic (like BERT)	Fully understands your situation
Instant	Finishes your words	Predicts your whole question
Multi-language	Works for big languages	Seamless for all languages
Multi-format	Mostly text	Combines text, images, and voice

The goal? Make searching as natural as asking a friend. These changes will make finding what you need online faster and more accurate than ever.

9. Conclusion

Query expansion has changed search engines. Here's what we've learned:

It uses user data to expand queries beyond keywords
Both user-marked and system-inferred feedback improve results
Real-time query expansion (RTQE) boosts initial query quality

These techniques have reshaped search engines:

AI and machine learning analyze user patterns for more accurate results
Autocomplete now predicts entire questions
Cross-language searching expands queries across languages

A University of Amsterdam study found that expanding searches across languages boosted results by up to 23% for Dutch-English searches.

Query expansion isn't just for web searches. It's making waves in:

1. E-commerce

AI personalization engines boost sales by showing products based on browsing history. During Cyber Monday, retargeting efforts drove successful conversions.

2. Healthcare

AI sifts through medical research to give users health info that fits their profiles.

3. Academic Research

Scopus lets users pick relevant fields and keywords to expand academic searches.

Field	Use of Query Expansion	Impact
E-commerce	Product recommendations	Increased sales
Healthcare	Personalized health info	Better patient education
Academic	Refined research queries	More relevant study results

As search tech improves, we can expect even more tailored, helpful results across all fields.

FAQs

What are search behaviors?

Search behaviors are how people use search engines. This includes:

Where they look on the page
How long they spend looking
What results they click
How they change their searches

Knowing these behaviors helps make search engines and websites better.

What is user intent in search engine?

User intent is the goal behind a search. It's what someone wants to do when they type something into Google.

There are four main types:

1. Informational

People want to learn something. For example: "How does photosynthesis work?"

2. Commercial

People are thinking about buying something. For example: "Best smartphones 2023"

3. Transactional

People are ready to buy or do something. For example: "Buy iPhone 14 Pro"

4. Navigational

People want to find a specific website. For example: "Facebook login"

Understanding user intent is crucial for:

Content creators: To make stuff people actually want
SEO pros: To use the right keywords
Search engines: To show the best results

User Behavior-Driven Query Expansion Techniques