forked from nerfies/nerfies.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.html
More file actions
588 lines (529 loc) · 19.9 KB
/
index.html
File metadata and controls
588 lines (529 loc) · 19.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="description"
content="SIGIR 2026 tutorial homepage for Bridging Personalization and AI: From RAG to Agent.">
<meta name="keywords"
content="SIGIR 2026, tutorial, personalization, RAG, LLM agents, information retrieval">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Bridging Personalization and AI: From RAG to Agent</title>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
<link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="./static/css/bulma-slider.min.css">
<link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="./static/css/index.css">
<style>
:root {
--accent: #0f766e;
--accent-soft: #dff5f2;
--accent-deep: #134e4a;
--gold: #b45309;
--ink: #1f2937;
--muted: #5b6472;
--line: #d9e2ec;
--panel: #ffffff;
--bg-top: #f7fbfa;
--bg-bottom: #fff9f0;
}
html {
scroll-behavior: smooth;
}
body {
color: var(--ink);
background:
radial-gradient(circle at top left, rgba(15, 118, 110, 0.10), transparent 32%),
radial-gradient(circle at top right, rgba(180, 83, 9, 0.10), transparent 28%),
linear-gradient(180deg, var(--bg-top) 0%, #ffffff 38%, var(--bg-bottom) 100%);
font-family: "Google Sans", "Noto Sans", sans-serif;
}
.navbar {
background: rgba(255, 255, 255, 0.88);
backdrop-filter: blur(10px);
border-bottom: 1px solid rgba(15, 118, 110, 0.10);
}
.navbar-item,
.navbar-link {
color: var(--ink);
font-weight: 600;
}
.page-tag {
display: inline-block;
margin-bottom: 1rem;
padding: 0.45rem 0.8rem;
border-radius: 999px;
background: var(--accent-soft);
color: var(--accent-deep);
font-size: 0.82rem;
font-weight: 700;
letter-spacing: 0.08em;
text-transform: uppercase;
}
.hero {
padding-top: 2rem;
}
.hero .container.is-max-widescreen {
max-width: 1180px;
}
.publication-title {
color: #102a43;
font-family: "Castoro", serif;
line-height: 1.1;
margin-bottom: 1rem;
}
.hero-copy {
color: var(--muted);
font-size: 1.1rem;
line-height: 1.75;
margin-bottom: 1.5rem;
}
.publication-authors .author-block {
display: inline-block;
margin-right: 0.45rem;
margin-bottom: 0.5rem;
}
.meta-row {
display: flex;
flex-wrap: wrap;
gap: 0.75rem;
margin: 1.25rem 0 1.75rem;
}
.meta-pill {
padding: 0.6rem 0.9rem;
border-radius: 999px;
background: rgba(255, 255, 255, 0.92);
border: 1px solid rgba(15, 118, 110, 0.14);
color: var(--accent-deep);
font-size: 0.95rem;
font-weight: 600;
}
.feature-card,
.section-card {
height: 100%;
background: rgba(255, 255, 255, 0.92);
border: 1px solid rgba(15, 118, 110, 0.10);
border-radius: 24px;
box-shadow: 0 18px 48px rgba(16, 42, 67, 0.08);
}
.feature-card {
padding: 1.5rem;
}
.feature-card h2,
.section-card h2 {
color: #102a43;
font-family: "Castoro", serif;
margin-bottom: 0.85rem;
}
.feature-card ul,
.section-card ul {
margin-left: 1.2rem;
color: var(--muted);
line-height: 1.8;
}
.section-card {
padding: 2rem;
}
.section-title {
color: #102a43;
font-family: "Castoro", serif;
margin-bottom: 1rem;
}
.section-card p {
color: var(--muted);
line-height: 1.85;
margin-bottom: 1rem;
}
.section-card p:last-child {
margin-bottom: 0;
}
.structure-frame {
padding: 1rem;
border-radius: 22px;
background: linear-gradient(180deg, #ffffff 0%, #f5fffd 100%);
border: 1px solid var(--line);
}
.structure-frame img {
display: block;
width: 100%;
height: auto;
border-radius: 16px;
}
.figure-note {
margin-top: 1rem;
color: var(--muted);
font-size: 0.96rem;
line-height: 1.7;
}
.table-container {
border-radius: 22px;
overflow: hidden;
border: 1px solid rgba(15, 118, 110, 0.12);
box-shadow: 0 18px 48px rgba(16, 42, 67, 0.08);
}
.schedule-table {
margin-bottom: 0;
background: rgba(255, 255, 255, 0.96);
}
.schedule-table thead th {
background: #ecfdf5;
color: var(--accent-deep);
border-color: rgba(15, 118, 110, 0.12);
font-size: 0.88rem;
letter-spacing: 0.05em;
text-transform: uppercase;
}
.schedule-table td,
.schedule-table th {
border-color: rgba(15, 118, 110, 0.10);
vertical-align: top;
}
.schedule-table td:first-child {
white-space: nowrap;
font-weight: 700;
color: #102a43;
width: 11rem;
}
.schedule-table td:nth-child(2) {
font-weight: 700;
color: var(--accent-deep);
width: 19rem;
}
.audience-grid .section-card,
.resource-grid .section-card {
min-height: 100%;
}
.presenter-list {
margin-left: 1.2rem;
color: var(--muted);
line-height: 1.9;
}
.presenter-list strong {
color: #102a43;
}
.footer-note {
color: var(--muted);
text-align: center;
padding-bottom: 3rem;
}
.button.is-dark.is-rounded {
background: #102a43;
}
.button.is-link.is-light {
color: var(--accent-deep);
background: var(--accent-soft);
}
@media screen and (max-width: 768px) {
.hero {
padding-top: 1rem;
}
.section-card,
.feature-card {
padding: 1.35rem;
}
.schedule-table td:first-child,
.schedule-table td:nth-child(2) {
width: auto;
white-space: normal;
}
}
</style>
</head>
<body>
<nav class="navbar is-fixed-top" role="navigation" aria-label="main navigation">
<div class="container is-max-desktop">
<div class="navbar-brand">
<a class="navbar-item" href="#top">
<strong>SIGIR 2026 Tutorial</strong>
</a>
<a role="button" class="navbar-burger" aria-label="menu" aria-expanded="false" data-target="navMenu">
<span aria-hidden="true"></span>
<span aria-hidden="true"></span>
<span aria-hidden="true"></span>
</a>
</div>
<div id="navMenu" class="navbar-menu">
<div class="navbar-end">
<a class="navbar-item" href="#abstract">Abstract</a>
<a class="navbar-item" href="#overview">Overview</a>
<a class="navbar-item" href="#schedule">Schedule</a>
<a class="navbar-item" href="#audience">Audience</a>
<!-- <a class="navbar-item" href="#presenters">Presenters</a> -->
</div>
</div>
</div>
</nav>
<section class="hero" id="top">
<div class="hero-body" style="padding-top: 6rem;">
<div class="container is-max-widescreen">
<div class="columns is-vcentered is-variable is-6">
<div class="column is-full">
<span class="page-tag">Proceedings of SIGIR '26</span>
<h1 class="title is-1 publication-title">Bridging Personalization and AI: From RAG to Agent</h1>
<p class="hero-copy">
A full-day tutorial on personalized Retrieval-Augmented Generation, user-adaptive retrieval,
memory-aware reasoning, and the evolution from RAG pipelines to personalized LLM-based agents.
</p>
<div class="is-size-5 publication-authors">
<span class="author-block">Pengyue Jia,</span>
<span class="author-block">Xiaopeng Li,</span>
<span class="author-block">Derong Xu,</span>
<span class="author-block">Yi Wen,</span>
<span class="author-block">Yingyi Zhang,</span>
<span class="author-block">Wenlin Zhang,</span>
<span class="author-block">Wanyu Wang,</span>
<span class="author-block">Yichao Wang,</span>
<span class="author-block">Yong Liu,</span>
<span class="author-block">Xiangyu Zhao</span>
</div>
<div class="is-size-6 publication-authors" style="color: var(--muted);">
<span class="author-block">City University of Hong Kong</span>
<span class="author-block">•</span>
<span class="author-block">Huawei Noah's Ark Lab</span>
</div>
<div class="meta-row">
<span class="meta-pill">July 20-24, 2026</span>
<span class="meta-pill">Melbourne, VIC, Australia</span>
<span class="meta-pill">Full-Day Tutorial</span>
</div>
<div class="buttons">
<a href="https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent"
class="button is-dark is-rounded is-medium">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>GitHub Repository</span>
</a>
<a href="#schedule" class="button is-link is-light is-rounded is-medium">
<span>View Schedule</span>
</a>
</div>
</div>
<!-- <div class="column is-5">
<div class="feature-card">
<h2 class="title is-4">At a Glance</h2>
<ul>
<li>A unified view of personalization across pre-retrieval, retrieval, and generation.</li>
<li>Direct connections between RAG stages and agent capabilities such as memory, planning, and tool use.</li>
<li>Coverage of datasets, evaluation metrics, privacy, robustness, and deployment trade-offs.</li>
<li>Practical resources, slides, and a continuously updated reading repository.</li>
</ul>
</div>
</div> -->
</div>
</div>
</div>
</section>
<section class="section" id="abstract">
<div class="container is-max-desktop">
<div class="section-card">
<h2 class="title is-3 section-title">Abstract</h2>
<p>
Personalization is becoming a core capability of modern AI systems. It enables systems to adapt
their responses and behaviors according to individual users' preferences, contexts, and goals.
Recent research has focused on Retrieval-Augmented Generation (RAG) and its development toward
more advanced agent-based frameworks to improve user satisfaction in personalized settings.
In this tutorial, we provide a systematic overview of how personalization can be incorporated
into the three main stages of RAG: pre-retrieval, retrieval, and generation. We then extend the
discussion to personalized LLM-based agents, which build on RAG by adding agent capabilities such
as user understanding, personalized planning and execution, and adaptive response generation.
For both RAG-based and agent-based approaches, we present clear definitions, review recent research,
and summarize commonly used datasets and evaluation metrics. We also discuss key challenges, current
limitations, and promising future research directions.
</p>
</div>
</div>
</section>
<section class="section" id="overview">
<div class="container is-max-desktop">
<div class="columns is-variable is-6">
<div class="column is-7">
<div class="section-card">
<h2 class="title is-3 section-title">Tutorial Structure</h2>
<div class="structure-frame">
<img src="./static/images/structure.jpg"
alt="Overview of the tutorial structure from personalized RAG to personalized agents">
</div>
<p class="figure-note">
The tutorial organizes personalization from the RAG pipeline into the broader agent workflow,
highlighting how user modeling, retrieval, generation, memory, planning, and execution fit into
one coherent framework.
</p>
</div>
</div>
<div class="column is-5">
<div class="section-card" style="margin-bottom: 1.5rem;">
<h2 class="title is-4 section-title">Learning Objectives</h2>
<ul>
<li>Understand the foundations of personalized RAG across pre-retrieval, retrieval, and generation.</li>
<li>Analyze how RAG components align with agent workflows such as user understanding, planning, execution, and response generation.</li>
<li>Identify key design trade-offs involving user modeling granularity, privacy, memory, efficiency, and robustness.</li>
<li>Explore emerging directions including continual personalization, multimodal personalization, and scalable deployment.</li>
</ul>
</div>
<!-- <div class="section-card">
<h2 class="title is-4 section-title">Materials and Resources</h2>
<p>
Slides, structured diagrams, unified formulations, and an annotated reading list will be made
publicly available through the tutorial repository.
</p>
<p>
Participants will also receive a curated bibliography of key papers, summaries of representative
datasets and evaluation benchmarks, and links to open-source implementations and related resources.
</p>
</div> -->
</div>
</div>
</div>
</section>
<section class="section" id="schedule">
<div class="container is-max-desktop">
<h2 class="title is-3 section-title has-text-centered">Schedule</h2>
<div class="table-container">
<table class="table is-fullwidth schedule-table">
<thead>
<tr>
<th>Time</th>
<th>Session</th>
<th>Topics</th>
</tr>
</thead>
<tbody>
<tr>
<td>09:00-09:20</td>
<td>Introduction to RAG and Agent</td>
<td>Overview of RAG, agentic RAG, and tutorial organization.</td>
</tr>
<tr>
<td>09:20-09:50</td>
<td>Personalization in RAG and Agents</td>
<td>Problem formulation and foundations of personalization.</td>
</tr>
<tr>
<td>09:50-10:30</td>
<td>Personalization in Pre-retrieval</td>
<td>Task formulation, query rewriting, and query expansion.</td>
</tr>
<tr>
<td>10:30-11:00</td>
<td>Coffee Break</td>
<td>Break.</td>
</tr>
<tr>
<td>11:00-11:50</td>
<td>Personalization in Retrieval</td>
<td>Task formulation, indexing, retrieval, and post-retrieval.</td>
</tr>
<tr>
<td>11:50-12:30</td>
<td>Personalization in Generation</td>
<td>Generation from explicit preferences and implicit preferences.</td>
</tr>
<tr>
<td>12:30-14:00</td>
<td>Coffee Break</td>
<td>Break.</td>
</tr>
<tr>
<td>14:00-15:00</td>
<td>From RAG to Agent</td>
<td>Relationship between RAG and agents, personalized understanding, planning, execution, and generation.</td>
</tr>
<tr>
<td>15:00-15:30</td>
<td>Evaluation and Datasets</td>
<td>Personalization evaluation metrics, datasets, and benchmarks.</td>
</tr>
<tr>
<td>15:30-16:00</td>
<td>Coffee Break</td>
<td>Break.</td>
</tr>
<tr>
<td>16:00-16:45</td>
<td>Challenges and Future Directions</td>
<td>Open problems, limitations, future opportunities, and conclusion.</td>
</tr>
<tr>
<td>16:45-17:30</td>
<td>Panel Discussion and Interactive Q&A</td>
<td>Open research discussion, academic and industry perspectives, and audience questions.</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section class="section" id="audience">
<div class="container is-max-desktop">
<div class="columns audience-grid is-variable is-6">
<div class="column is-6">
<div class="section-card">
<h2 class="title is-4 section-title">Target Audience</h2>
<p>
This tutorial is intended for researchers, practitioners, and AI enthusiasts who are exploring
personalization in RAG and agent-based systems.
</p>
<p>
It is particularly relevant to attendees working on information retrieval, recommender systems,
LLM applications, conversational AI, search, and interactive intelligent systems.
</p>
</div>
</div>
<div class="column is-6">
<div class="section-card">
<h2 class="title is-4 section-title">Prerequisites</h2>
<p>
Participants are expected to have a basic understanding of machine learning and information retrieval.
</p>
<p>
The material is structured to remain accessible to advanced undergraduate students while still being
useful for industry professionals and researchers seeking a deeper understanding of personalized RAG
and agentic systems.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- <section class="section" id="presenters">
<div class="container is-max-desktop">
<div class="section-card">
<h2 class="title is-3 section-title">Presenters</h2>
<ul class="presenter-list">
<li><strong>Pengyue Jia</strong> is a fourth-year PhD candidate at City University of Hong Kong, with publications at KDD, NeurIPS, AAAI, SIGIR, ACL, and WWW, and prior tutorial experience at IJCAI, WWW, and KDD.</li>
<li><strong>Xiaopeng Li</strong> is a fourth-year PhD candidate at City University of Hong Kong working on information retrieval, recommendation, personalized RAG, and LLM-based retrieval.</li>
<li><strong>Derong Xu</strong> is a joint PhD student at USTC and CityU whose research focuses on LLMs, knowledge graphs, and retrieval-augmented generation.</li>
<li><strong>Yi Wen</strong>, <strong>Yingyi Zhang</strong>, <strong>Wenlin Zhang</strong>, and <strong>Wanyu Wang</strong> contribute expertise in recommendation, personalized LLMs, RAG, and user modeling.</li>
<li><strong>Yichao Wang</strong> is a principal researcher at Huawei Noah's Ark Lab with extensive work on deep learning, RAG, personalized agents, and LLMs for recommendation.</li>
<li><strong>Yong Liu</strong> is an expert researcher at Huawei Noah's Ark Lab whose recent work spans LLM agents, RAG, and generative search and recommendation.</li>
<li><strong>Xiangyu Zhao</strong> is a tenured Associate Professor at City University of Hong Kong whose research focuses on personalization, information retrieval, machine learning, and large language models.</li>
</ul>
</div>
</div>
</section> -->
<section class="section">
<div class="container is-max-desktop">
<p class="footer-note">
Tutorial resources and updates: <a href="https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent">Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent</a>
</p>
</div>
</section>
<script>
document.addEventListener('DOMContentLoaded', function () {
var burgers = Array.prototype.slice.call(document.querySelectorAll('.navbar-burger'), 0);
burgers.forEach(function (el) {
el.addEventListener('click', function () {
var target = el.dataset.target;
var menu = document.getElementById(target);
el.classList.toggle('is-active');
menu.classList.toggle('is-active');
});
});
});
</script>
</body>
</html>